Live time tissue classification using electrical parameters

ABSTRACT

A radio frequency (RF) instrument may include a method of classifying a tissue in live time. The method may include activating the instrument for a first period of time T 1  when the RF instrument contacts the tissue, plotting at least three electrical parameters associated with the tissue to classify the tissue into distinct groups, and applying a classification algorithm to classify the tissue into a distinct group in live time. The parameters may include an initial impedance of the tissue, a minimum impedance of the tissue, and an amount of time that the impedance slope is ˜0. The instrument may collect the parameters during a predetermined amount of time, such as within the first 0.75 seconds of the activation of the device. The classification algorithm may include a support vector machine algorithm that may use a linear, polynomial, or radial basis set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application Ser. No. 62/640,417, titled TEMPERATURE CONTROL IN ULTRASONIC DEVICE AND CONTROL SYSTEM THEREFOR, filed Mar. 8, 2018, and to U.S. Provisional Patent Application Ser. No. 62/640,415, titled ESTIMATING STATE OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR, filed Mar. 8, 2018, the disclosure of each of which is herein incorporated by reference in its entirety.

BACKGROUND

In a surgical environment, smart energy devices may be needed in a smart energy architecture environment. Ultrasonic surgical devices, such as ultrasonic scalpels, are finding increasingly widespread applications in surgical procedures by virtue of their unique performance characteristics. Depending upon specific device configurations and operational parameters, ultrasonic surgical devices can provide substantially simultaneous transection of tissue and homeostasis by coagulation, desirably minimizing patient trauma. An ultrasonic surgical device may comprise a handpiece containing an ultrasonic transducer, and an instrument coupled to the ultrasonic transducer having a distally-mounted end effector (e.g., a blade tip) to cut and seal tissue. In some cases, the instrument may be permanently affixed to the handpiece. In other cases, the instrument may be detachable from the handpiece, as in the case of a disposable instrument or an interchangeable instrument. The end effector transmits ultrasonic energy to tissue brought into contact with the end effector to realize cutting and sealing action. Ultrasonic surgical devices of this nature can be configured for open surgical use, laparoscopic, or endoscopic surgical procedures including robotic-assisted procedures.

Ultrasonic energy cuts and coagulates tissue using temperatures lower than those used in electrosurgical procedures and can be transmitted to the end effector by an ultrasonic generator in communication with the handpiece. Vibrating at high frequencies (e.g., 55,500 cycles per second), the ultrasonic blade denatures protein in the tissue to form a sticky coagulum. Pressure exerted on tissue by the blade surface collapses blood vessels and allows the coagulum to form a hemostatic seal. A surgeon can control the cutting speed and coagulation by the force applied to the tissue by the end effector, the time over which the force is applied, and the selected excursion level of the end effector.

The ultrasonic transducer may be modeled as an equivalent circuit comprising a first branch having a static capacitance and a second “motional” branch having a serially connected inductance, resistance and capacitance that define the electromechanical properties of a resonator. Known ultrasonic generators may include a tuning inductor for tuning out the static capacitance at a resonant frequency so that substantially all of a generator's drive signal current flows into the motional branch. Accordingly, by using a tuning inductor, the generator's drive signal current represents the motional branch current, and the generator is thus able to control its drive signal to maintain the ultrasonic transducer's resonant frequency. The tuning inductor may also transform the phase impedance plot of the ultrasonic transducer to improve the generator's frequency lock capabilities. However, the tuning inductor must be matched with the specific static capacitance of an ultrasonic transducer at the operational resonant frequency. In other words, a different ultrasonic transducer having a different static capacitance requires a different tuning inductor.

Additionally, in some ultrasonic generator architectures, the generator's drive signal exhibits asymmetrical harmonic distortion that complicates impedance magnitude and phase measurements. For example, the accuracy of impedance phase measurements may be reduced due to harmonic distortion in the current and voltage signals.

Moreover, electromagnetic interference in noisy environments decreases the ability of the generator to maintain lock on the ultrasonic transducer's resonant frequency, increasing the likelihood of invalid control algorithm inputs.

Electrosurgical devices for applying electrical energy to tissue in order to treat and/or destroy the tissue are also finding increasingly widespread applications in surgical procedures. An electrosurgical device may comprise a handpiece and an instrument having a distally-mounted end effector (e.g., one or more electrodes). The end effector can be positioned against the tissue such that electrical current is introduced into the tissue. Electrosurgical devices can be configured for bipolar or monopolar operation. During bipolar operation, current is introduced into and returned from the tissue by active and return electrodes, respectively, of the end effector. During monopolar operation, current is introduced into the tissue by an active electrode of the end effector and returned through a return electrode (e.g., a grounding pad) separately located on a patient's body. Heat generated by the current flowing through the tissue may form hemostatic seals within the tissue and/or between tissues and thus may be particularly useful for sealing blood vessels, for example. The end effector of an electrosurgical device may also comprise a cutting member that is movable relative to the tissue and the electrodes to transect the tissue.

Electrical energy applied by an electrosurgical device can be transmitted to the instrument by a generator in communication with the handpiece. The electrical energy may be in the form of radio frequency (RF) energy. RF energy is a form of electrical energy that may be in the frequency range of 300 kHz to 1 MHz, as described in EN60601-2-2:2009+A11:2011, Definition 201.3.218—HIGH FREQUENCY. For example, the frequencies in monopolar RF applications are typically restricted to less than 5 MHz. However, in bipolar RF applications, the frequency can be almost any value. Frequencies above 200 kHz are typically used for monopolar applications in order to avoid the unwanted stimulation of nerves and muscles which would result from the use of low frequency current. Lower frequencies may be used for bipolar techniques if a risk analysis shows the possibility of neuromuscular stimulation has been mitigated to an acceptable level. Normally, frequencies above 5 MHz are not used in order to minimize the problems associated with high frequency leakage currents. It is generally recognized that 10 mA is the lower threshold of thermal effects on tissue.

During its operation, an electrosurgical device can transmit low frequency RF energy through tissue, which causes ionic agitation, or friction, in effect resistive heating, thereby increasing the temperature of the tissue. Because a sharp boundary may be created between the affected tissue and the surrounding tissue, surgeons can operate with a high level of precision and control, without sacrificing un-targeted adjacent tissue. The low operating temperatures of RF energy may be useful for removing, shrinking, or sculpting soft tissue while simultaneously sealing blood vessels. RF energy may work particularly well on connective tissue, which is primarily comprised of collagen and shrinks when contacted by heat.

Due to their unique drive signal, sensing and feedback needs, ultrasonic and electrosurgical devices have generally required different generators. Additionally, in cases where the instrument is disposable or interchangeable with a handpiece, ultrasonic and electrosurgical generators are limited in their ability to recognize the particular instrument configuration being used and to optimize control and diagnostic processes accordingly. Moreover, capacitive coupling between the non-isolated and patient-isolated circuits of the generator, especially in cases where higher voltages and frequencies are used, may result in exposure of a patient to unacceptable levels of leakage current.

Furthermore, due to their unique drive signal, sensing and feedback needs, ultrasonic and electrosurgical devices have generally required different user interfaces for the different generators. In such conventional ultrasonic and electrosurgical devices, one user interface is configured for use with an ultrasonic instrument whereas a different user interface may be configured for use with an electrosurgical instrument. Such user interfaces include hand and/or foot activated user interfaces such as hand activated switches and/or foot activated switches. As various aspects of combined generators for use with both ultrasonic and electrosurgical instruments are contemplated in the subsequent disclosure, additional user interfaces that are configured to operate with both ultrasonic and/or electrosurgical instrument generators also are contemplated.

Additional user interfaces for providing feedback, whether to the user or other machine, are contemplated within the subsequent disclosure to provide feedback indicating an operating mode or status of either an ultrasonic and/or electrosurgical instrument. Providing user and/or machine feedback for operating a combination ultrasonic and/or electrosurgical instrument will require providing sensory feedback to a user and electrical/mechanical/electro-mechanical feedback to a machine. Feedback devices that incorporate visual feedback devices (e.g., an LCD display screen, LED indicators), audio feedback devices (e.g., a speaker, a buzzer) or tactile feedback devices (e.g., haptic actuators) for use in combined ultrasonic and/or electrosurgical instruments are contemplated in the subsequent disclosure.

Other electrical surgical instruments include, without limitation, irreversible and/or reversible electroporation, and/or microwave technologies, among others. Accordingly, the techniques disclosed herein are applicable to ultrasonic, bipolar or monopolar RF (electrosurgical), irreversible and/or reversible electroporation, and/or microwave based surgical instruments, among others.

SUMMARY

An aspect of a method of classifying a tissue in live time may include activating, by a processor or control circuit, a radio frequency (RF) instrument for a first period of time T1, wherein the RF instrument contacts the tissue, plotting, by the processor or control circuit, at least three electrical parameters associated with the tissue in contact with the RF instrument to classify the tissue into distinct groups, and applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time.

In one aspect of the method, plotting, by the processor or control circuit, at least three electrical parameters associated with the tissue in contact with the RF instrument my include plotting, by the processor or control circuit, an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that the RF impedance slope is ˜0.

One aspect of the method may further include collecting, by the processor or control circuit, data associated with the at least three electrical parameters in a predetermined amount of time.

In one aspect of the method, collecting, by the processor or control circuit, data associated with the at least three electrical parameters in a predetermined amount of time my include collecting, by the processor or control circuit, data associated with the at least three electrical parameters in within a first 0.75 seconds after activation of the radio frequency (RF) instrument.

In one aspect of the method, applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time may include applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time using a support vector machine algorithm.

In one aspect of the method, applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time using a support vector machine algorithm my include applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time using a linear basis function, a polynomial basis function, or a radial basis function.

In one aspect, the method may further include applying, by the processor or control circuit, an activation algorithm specific to each tissue group after the first period T1.

An aspect of a surgical instrument may include a radio frequency (RF) instrument having an end effector and a generator configured to supply power to the end effector. An aspect of the generator may include a control circuit configured to activate the radio frequency (RF) instrument for a first period of time T1, wherein the RF instrument contacts the tissue, plot at least three electrical parameters associated with the tissue in contact with the RF instrument to classify the tissue into distinct groups, and apply a classification algorithm to classify the tissue into a distinct group in live time.

In one aspect of the surgical instrument, the at least three electrical parameters associated with the tissue in contact with the RF instrument my include an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that the RF impedance slope is ˜0.

In one aspect of the surgical instrument, the control circuit is further configured to collect data associated with the at least three electrical parameters in a predetermined amount of time.

In one aspect of the surgical instrument, the predetermined amount of time may include a first 0.75 seconds after activation of the radio frequency (RF) instrument.

In one aspect of the surgical instrument, the control circuit is further configured to classify the tissue into a distinct group in live time using a support vector machine algorithm.

In one aspect of the surgical instrument, the support vector machine algorithm may include a linear basis function, a polynomial basis function, or a radial basis function.

In one aspect of the surgical instrument, the control circuit is further configured to apply an activation algorithm specific to each tissue group after the first period T1.

An aspect of a generator for a surgical instrument in which the surgical instrument includes a radio frequency (RF) instrument having an end effector, may include a control circuit configured to activate the radio frequency (RF) instrument for a first period of time T1, wherein the RF instrument contacts a tissue, plot at least three electrical parameters associated with the tissue in contact with the RF instrument to classify the tissue into distinct groups, and apply a classification algorithm to classify the tissue into a distinct group in live time.

In one aspect of the generator for a surgical instrument, the at least three electrical parameters associated with the tissue in contact with the RF instrument may include an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that the RF impedance slope is ˜0.

In one aspect of the generator for a surgical instrument, the control circuit is further configured to collect data associated with the at least three electrical parameters in a predetermined amount of time.

In one aspect of the generator for a surgical instrument, the predetermined amount of time comprises a first 0.75 seconds after activation of the radio frequency (RF) instrument.

In one aspect of the generator for a surgical instrument, the control circuit is further configured to classify the tissue into a distinct group in live time using a support vector machine algorithm.

In one aspect of the generator for a surgical instrument, the support vector machine algorithm may include a linear basis function, a polynomial basis function, or a radial basis function.

In one aspect of the generator for a surgical instrument, the control circuit is further configured to apply an activation algorithm specific to each tissue group after the first period T1.

FIGURES

The features of various aspects are set forth with particularity in the appended claims. The various aspects, however, both as to organization and methods of operation, together with further objects and advantages thereof, may best be understood by reference to the following description, taken in conjunction with the accompanying drawings as follows.

FIG. 1 is a system configured to execute adaptive ultrasonic blade control algorithms in a surgical data network comprising a modular communication hub, in accordance with at least one aspect of the present disclosure.

FIG. 2 illustrates an example of a generator, in accordance with at least one aspect of the present disclosure.

FIG. 3 is a surgical system comprising a generator and various surgical instruments usable therewith, in accordance with at least one aspect of the present disclosure.

FIG. 4 is an end effector, in accordance with at least one aspect of the present disclosure.

FIG. 5 is a diagram of the surgical system of FIG. 3, in accordance with at least one aspect of the present disclosure.

FIG. 6 is a model illustrating motional branch current, in accordance with at least one aspect of the present disclosure.

FIG. 7 is a structural view of a generator architecture, in accordance with at least one aspect of the present disclosure.

FIGS. 8A-8C are functional views of a generator architecture, in accordance with at least one aspect of the present disclosure.

FIGS. 9A-9B are structural and functional aspects of a generator, in accordance with at least one aspect of the present disclosure.

FIG. 10 illustrates a control circuit configured to control aspects of the surgical instrument or tool, in accordance with at least one aspect of the present disclosure.

FIG. 11 illustrates a combinational logic circuit configured to control aspects of the surgical instrument or tool, in accordance with at least one aspect of the present disclosure.

FIG. 12 illustrates a sequential logic circuit configured to control aspects of the surgical instrument or tool, in accordance with at least one aspect of the present disclosure.

FIG. 13 illustrates one aspect of a fundamental architecture for a digital synthesis circuit such as a direct digital synthesis (DDS) circuit configured to generate a plurality of wave shapes for the electrical signal waveform for use in a surgical instrument, in accordance with at least one aspect of the present disclosure.

FIG. 14 illustrates one aspect of direct digital synthesis (DDS) circuit configured to generate a plurality of wave shapes for the electrical signal waveform for use in surgical instrument, in accordance with at least one aspect of the present disclosure.

FIG. 15 illustrates one cycle of a discrete time digital electrical signal waveform, in accordance with at least one aspect of the present disclosure of an analog waveform (shown superimposed over a discrete time digital electrical signal waveform for comparison purposes), in accordance with at least one aspect of the present disclosure.

FIG. 16 is a diagram of a control system in accordance with one aspect of this disclosure.

FIG. 17 illustrates a proportional-integral-derivative (PID) controller feedback control system in accordance with one aspect of this disclosure.

FIG. 18 is an alternative system for controlling the frequency of an ultrasonic electromechanical system and detecting the impedance thereof, in accordance with at least one aspect of the present disclosure.

FIG. 19 is a spectra of the same ultrasonic device with a variety of different states and conditions of the end effector where phase and magnitude of the impedance of an ultrasonic transducer are plotted as a function of frequency, in accordance with at least one aspect of the present disclosure.

FIG. 20 is a graphical representation of a plot of a set of 3D training data S, where ultrasonic transducer impedance magnitude and phase are plotted as a function of frequency, in accordance with at least one aspect of the present disclosure.

FIG. 21 is a logic flow diagram depicting a control program or a logic configuration to determine jaw conditions based on the complex impedance characteristic pattern (fingerprint), in accordance with at least one aspect of the present disclosure.

FIG. 22 is a circle plot of complex impedance plotted as an imaginary component versus real components of a piezoelectric vibrator, in accordance with at least one aspect of the present disclosure.

FIG. 23 is a circle plot of complex admittance plotted as an imaginary component versus real components of a piezoelectric vibrator, in accordance with at least one aspect of the present disclosure.

FIG. 24 is a circle plot of complex admittance for a 55.5 kHz ultrasonic piezoelectric transducer.

FIG. 25 is a graphical display of an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the jaw open and no loading where complex admittance is depicted in broken line and complex impedance is depicted in solid line, in accordance with at least one aspect of the present disclosure.

FIG. 26 is a graphical display of an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the jaw clamped on dry chamois where complex admittance is depicted in broken line and complex impedance is depicted in solid line, in accordance with at least one aspect of the present disclosure.

FIG. 27 is a graphical display of an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the jaw tip clamped on moist chamois where complex admittance is depicted in broken line and complex impedance is depicted in solid line, in accordance with at least one aspect of the present disclosure.

FIG. 28 is a graphical display of an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the jaw fully clamped on moist chamois where complex admittance is depicted in broken line and complex impedance is depicted in solid line, in accordance with at least one aspect of the present disclosure.

FIG. 29 is a graphical display of an impedance analyzer showing impedance/admittance plots where frequency is swept from 48 kHz to 62 kHz to capture multiple resonances of an ultrasonic device with the jaw open where the rectangular overlay shown in broken line is to help see the circles, in accordance with at least one aspect of the present disclosure.

FIG. 30 is a logic flow diagram of a process depicting a control program or a logic configuration to determine jaw conditions based on estimates of the radius and offsets of an impedance/admittance circle, in accordance with at least one aspect of the present disclosure.

FIG. 31 is a three-dimensional graphical representation of tissue radio frequency (RF) impedance classification, in accordance with at least one aspect of the present disclosure.

FIG. 32 is a three-dimensional graphical representation of tissue radio frequency (RF) impedance analysis, in accordance with at least one aspect of the present disclosure.

DESCRIPTION

Applicant of the present patent application also owns the following U.S. patent applications filed Sep. 27, 2018, each of which is herein incorporated by reference in its entirety:

-   -   U.S. Patent Application Ser. No. 16/144,335, titled METHODS FOR         CONTROLLING TEMPERATURE IN ULTRASONIC DEVICE, now U.S. Patent         Application Publication No. 2019/0274717;     -   U.S. Patent Application Ser. No. 16/144,345, titled ULTRASONIC         SEALING ALGORITHM WITH TEMPERATURE CONTROL, now U.S. Patent         Application Publication No. 2019/0274718;     -   U.S. Patent Application Ser. No. 16/144,351, titled APPLICATION         OF SMART ULTRASONIC BLADE TECHNOLOGY, now U.S. Patent         Application Publication No. 2019/0274705;     -   U.S. Patent Application Ser. No. 16/144,423, titled ADAPTIVE         ADVANCED TISSUE TREATMENT PAD SAVER MODE, now U.S. Patent         Application Publication No. 2019/0274709;     -   U.S. Patent Application Ser. No. 16/144,445, titled SMART BLADE         TECHNOLOGY TO CONTROL BLADE INSTABILITY, now U.S. Patent         Application Publication No. 2019/0274712; and     -   U.S. Patent Application Ser. No. 16/144,483, titled START         TEMPERATURE OF BLADE, now U.S. Patent Application Publication         No. 2019/0274720.

Applicant of the present patent application also owns the following U.S. patent applications filed Sep. 27, 2018, each of which is herein incorporated by reference in its entirety:

-   -   U.S. Patent Application Ser. No. 16/144,383 titled METHODS FOR         ESTIMATING AND CONTROLLING STATE OF ULTRASONIC END EFFECTOR, now         U.S. Patent Application Publication No. 2019/0274706;     -   U.S. Patent Application Ser. No. 16/144,391, titled IN-THE-JAW         CLASSIFIER BASED ON MODEL, now U.S. Patent Application         Publication No. 2019/0274719;     -   U.S. Patent Application Ser. No. 16/144,397, titled APPLICATION         OF SMART BLADE TECHNOLOGY, now U.S. Patent Application         Publication No. 2019/0274707;     -   U.S. Patent Application Ser. No. 16/144,405, titled SMART BLADE         AND POWER PULSING, now U.S. Patent Application Publication No.         2019/0274708;     -   U.S. Patent Application Ser. No. 16/144,418, titled ADJUSTMENT         OF COMPLEX IMPEDANCE TO COMPENSATE FOR LOST POWER IN AN         ARTICULATING ULTRASONIC DEVICE, now U.S. Patent Application         Publication No. 2019/0274662;     -   U.S. Patent Application Ser. No. 16/144,427, titled USING         SPECTROSCOPY TO DETERMINE DEVICE USE STATE IN COMBO INSTRUMENT,         now U.S. Patent Application Publication No. 2019/0274710;     -   U.S. Patent Application Ser. No. 16/144,434, titled VESSEL         SENSING FOR ADAPTIVE ADVANCED HEMOSTASIS, now U.S. Patent         Application Publication No. 2019/0274711;     -   U.S. Patent Application Ser. No. 16/144,460, titled CALCIFIED         VESSEL IDENTIFICATION, now U.S. Patent Application Publication         No. 2019/0274713;     -   U.S. Patent Application Ser. No. 16/144,472, titled DETECTION OF         LARGE VESSELS DURING PARENCHYMAL DISSECTION USING A SMART BLADE,         now U.S. Patent Application Publication No. 2019/0274749;     -   U.S. Patent Application Ser. No. 16/144,478, titled SMART BLADE         APPLICATION FOR REUSABLE AND DISPOSABLE DEVICES, now U.S. Patent         Application Publication No. 2019/0274714; and     -   U.S. Patent Application Ser. No. 16/144,508, titled FINE         DISSECTION MODE FOR TISSUE CLASSIFICATION, now U.S. Patent         Application Publication No. 2019/0274752.

Applicant of the present application owns the following U.S. Patent Applications, filed on Sep. 10, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. 62/729,177, titled         AUTOMATED DATA SCALING, ALIGNMENT, AND ORGANIZING BASED ON         PREDEFINED PARAMETERS WITHIN A SURGICAL NETWORK BEFORE         TRANSMISSION;     -   U.S. provisional Patent Application Ser. No. 62/729,182, titled         SENSING THE PATIENT POSITION AND CONTACT UTILIZING THE         MONO-POLAR RETURN PAD ELECTRODE TO PROVIDE SITUATIONAL AWARENESS         TO THE HUB;     -   U.S. Provisional Patent Application Ser. No. 62/729,184, titled         POWERED SURGICAL TOOL WITH A PREDEFINED ADJUSTABLE CONTROL         ALGORITHM FOR CONTROLLING AT LEAST ONE END-EFFECTOR PARAMETER         AND A MEANS FOR LIMITING THE ADJUSTMENT;     -   U.S. Provisional Patent Application Ser. No. 62/729,183, titled         SURGICAL NETWORK RECOMMENDATIONS FROM REAL TIME ANALYSIS OF         PROCEDURE VARIABLES AGAINST A BASELINE HIGHLIGHTING DIFFERENCES         FROM THE OPTIMAL SOLUTION;     -   U.S. Provisional Patent Application Ser. No. 62/729,191, titled         A CONTROL FOR A SURGICAL NETWORK OR SURGICAL NETWORK CONNECTED         DEVICE THAT ADJUSTS ITS FUNCTION BASED ON A SENSED SITUATION OR         USAGE;     -   U.S. Provisional Patent Application Ser. No. 62/729,176, titled         INDIRECT COMMAND AND CONTROL OF A FIRST OPERATING ROOM SYSTEM         THROUGH THE USE OF A SECOND OPERATING ROOM SYSTEM WITHIN A         STERILE FIELD WHERE THE SECOND OPERATING ROOM SYSTEM HAS PRIMARY         AND SECONDARY OPERATING MODES;     -   U.S. Provisional Patent Application Ser. No. 62/729,186, titled         WIRELESS PAIRING OF A SURGICAL DEVICE WITH ANOTHER DEVICE WITHIN         A STERILE SURGICAL FIELD BASED ON THE USAGE AND SITUATIONAL         AWARENESS OF DEVICES; and     -   U.S. Provisional Patent Application Ser. No. 62/729,185, titled         POWERED STAPLING DEVICE THAT IS CAPABLE OF ADJUSTING FORCE,         ADVANCEMENT SPEED, AND OVERALL STROKE OF CUTTING MEMBER OF THE         DEVICE BASED ON SENSED PARAMETER OF FIRING OR CLAMPING.

Applicant of the present application owns the following U.S. Patent Applications, filed on Aug. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. patent application Ser. No. 16/115,214, titled ESTIMATING         STATE OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR;     -   U.S. patent application Ser. No. 16/115,205, titled TEMPERATURE         CONTROL OF ULTRASONIC END EFFECTOR AND CONTROL SYSTEM THEREFOR;     -   U.S. patent application Ser. No. 16/115,233, titled RADIO         FREQUENCY ENERGY DEVICE FOR DELIVERING COMBINED ELECTRICAL         SIGNALS;     -   U.S. patent application Ser. No. 16/115,208, titled CONTROLLING         AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO TISSUE LOCATION;     -   U.S. patent application Ser. No. 16/115,220, titled CONTROLLING         ACTIVATION OF AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO THE         PRESENCE OF TISSUE;     -   U.S. patent application Ser. No. 16/115,232, titled DETERMINING         TISSUE COMPOSITION VIA AN ULTRASONIC SYSTEM;     -   U.S. patent application Ser. No. 16/115,239, titled DETERMINING         THE STATE OF AN ULTRASONIC ELECTROMECHANICAL SYSTEM ACCORDING TO         FREQUENCY SHIFT;     -   U.S. patent application Ser. No. 16/115,247, titled DETERMINING         THE STATE OF AN ULTRASONIC END EFFECTOR;     -   U.S. patent application Ser. No. 16/115,211, titled SITUATIONAL         AWARENESS OF ELECTROSURGICAL SYSTEMS;     -   U.S. patent application Ser. No. 16/115,226, titled MECHANISMS         FOR CONTROLLING DIFFERENT ELECTROMECHANICAL SYSTEMS OF AN         ELECTROSURGICAL INSTRUMENT;     -   U.S. patent application Ser. No. 16/115,240, titled DETECTION OF         END EFFECTOR EMERSION IN LIQUID;     -   U.S. patent application Ser. No. 16/115,249, titled INTERRUPTION         OF ENERGY DUE TO INADVERTENT CAPACITIVE COUPLING;     -   U.S. patent application Ser. No. 16/115,256, titled INCREASING         RADIO FREQUENCY TO CREATE PAD-LESS MONOPOLAR LOOP;     -   U.S. patent application Ser. No. 16/115,223, titled BIPOLAR         COMBINATION DEVICE THAT AUTOMATICALLY ADJUSTS PRESSURE BASED ON         ENERGY MODALITY; and     -   U.S. patent application Ser. No. 16/115,238, titled ACTIVATION         OF ENERGY DEVICES.

Applicant of the present application owns the following U.S. Patent Applications, filed on Aug. 23, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application No. 62/721,995, titled         CONTROLLING AN ULTRASONIC SURGICAL INSTRUMENT ACCORDING TO         TISSUE LOCATION;     -   U.S. Provisional Patent Application No. 62/721,998, titled         SITUATIONAL AWARENESS OF ELECTROSURGICAL SYSTEMS;     -   U.S. Provisional Patent Application No. 62/721,999, titled         INTERRUPTION OF ENERGY DUE TO INADVERTENT CAPACITIVE COUPLING;     -   U.S. Provisional Patent Application No. 62/721,994, titled         BIPOLAR COMBINATION DEVICE THAT AUTOMATICALLY ADJUSTS PRESSURE         BASED ON ENERGY MODALITY; and     -   U.S. Provisional Patent Application No. 62/721,996, titled RADIO         FREQUENCY ENERGY DEVICE FOR DELIVERING COMBINED ELECTRICAL         SIGNALS.

Applicant of the present application owns the following U.S. Patent Applications, filed on Jun. 30, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application No. 62/692,747, titled SMART         ACTIVATION OF AN ENERGY DEVICE BY ANOTHER DEVICE;     -   U.S. Provisional Patent Application No. 62/692,748, titled SMART         ENERGY ARCHITECTURE; and     -   U.S. Provisional Patent Application No. 62/692,768, titled SMART         ENERGY DEVICES.

Applicant of the present application owns the following U.S. Patent Applications, filed on Jun. 29, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. patent application Ser. No. 16/024,090, titled CAPACITIVE         COUPLED RETURN PATH PAD WITH SEPARABLE ARRAY ELEMENTS;     -   U.S. patent application Ser. No. 16/024,057, titled CONTROLLING         A SURGICAL INSTRUMENT ACCORDING TO SENSED CLOSURE PARAMETERS;     -   U.S. patent application Ser. No. 16/024,067, titled SYSTEMS FOR         ADJUSTING END EFFECTOR PARAMETERS BASED ON PERIOPERATIVE         INFORMATION;     -   U.S. patent application Ser. No. 16/024,075, titled SAFETY         SYSTEMS FOR SMART POWERED SURGICAL STAPLING;     -   U.S. patent application Ser. No. 16/024,083, titled SAFETY         SYSTEMS FOR SMART POWERED SURGICAL STAPLING;     -   U.S. patent application Ser. No. 16/024,094, titled SURGICAL         SYSTEMS FOR DETECTING END EFFECTOR TISSUE DISTRIBUTION         IRREGULARITIES;     -   U.S. patent application Ser. No. 16/024,138, titled SYSTEMS FOR         DETECTING PROXIMITY OF SURGICAL END EFFECTOR TO CANCEROUS         TISSUE;     -   U.S. patent application Ser. No. 16/024,150, titled SURGICAL         INSTRUMENT CARTRIDGE SENSOR ASSEMBLIES;     -   U.S. patent application Ser. No. 16/024,160, titled VARIABLE         OUTPUT CARTRIDGE SENSOR ASSEMBLY;     -   U.S. patent application Ser. No. 16/024,124, titled SURGICAL         INSTRUMENT HAVING A FLEXIBLE ELECTRODE;     -   U.S. patent application Ser. No. 16/024,132, titled SURGICAL         INSTRUMENT HAVING A FLEXIBLE CIRCUIT;     -   U.S. patent application Ser. No. 16/024,141, titled SURGICAL         INSTRUMENT WITH A TISSUE MARKING ASSEMBLY;     -   U.S. patent application Ser. No. 16/024,162, titled SURGICAL         SYSTEMS WITH PRIORITIZED DATA TRANSMISSION CAPABILITIES;     -   U.S. patent application Ser. No. 16/024,066, titled SURGICAL         EVACUATION SENSING AND MOTOR CONTROL;     -   U.S. patent application Ser. No. 16/024,096, titled SURGICAL         EVACUATION SENSOR ARRANGEMENTS;     -   U.S. patent application Ser. No. 16/024,116, titled SURGICAL         EVACUATION FLOW PATHS;     -   U.S. patent application Ser. No. 16/024,149, titled SURGICAL         EVACUATION SENSING AND GENERATOR CONTROL;     -   U.S. patent application Ser. No. 16/024,180, titled SURGICAL         EVACUATION SENSING AND DISPLAY;     -   U.S. patent application Ser. No. 16/024,245, titled         COMMUNICATION OF SMOKE EVACUATION SYSTEM PARAMETERS TO HUB OR         CLOUD IN SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL         PLATFORM;     -   U.S. patent application Ser. No. 16/024,258, titled SMOKE         EVACUATION SYSTEM INCLUDING A SEGMENTED CONTROL CIRCUIT FOR         INTERACTIVE SURGICAL PLATFORM;     -   U.S. patent application Ser. No. 16/024,265, titled SURGICAL         EVACUATION SYSTEM WITH A COMMUNICATION CIRCUIT FOR COMMUNICATION         BETWEEN A FILTER AND A SMOKE EVACUATION DEVICE; and     -   U.S. patent application Ser. No. 16/024,273, titled DUAL         IN-SERIES LARGE AND SMALL DROPLET FILTERS.

Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Jun. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. 62/691,228, titled         A METHOD OF USING REINFORCED FLEX CIRCUITS WITH MULTIPLE SENSORS         WITH ELECTROSURGICAL DEVICES;     -   U.S. Provisional Patent Application Ser. No. 62/691,227, titled         CONTROLLING A SURGICAL INSTRUMENT ACCORDING TO SENSED CLOSURE         PARAMETERS;     -   U.S. Provisional Patent Application Ser. No. 62/691,230, titled         SURGICAL INSTRUMENT HAVING A FLEXIBLE ELECTRODE;     -   U.S. Provisional Patent Application Ser. No. 62/691,219, titled         SURGICAL EVACUATION SENSING AND MOTOR CONTROL;     -   U.S. Provisional Patent Application Ser. No. 62/691,257, titled         COMMUNICATION OF SMOKE EVACUATION SYSTEM PARAMETERS TO HUB OR         CLOUD IN SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL         PLATFORM;     -   U.S. Provisional Patent Application Ser. No. 62/691,262, titled         SURGICAL EVACUATION SYSTEM WITH A COMMUNICATION CIRCUIT FOR         COMMUNICATION BETWEEN A FILTER AND A SMOKE EVACUATION DEVICE;         and     -   U.S. Provisional Patent Application Ser. No. 62/691,251, titled         DUAL IN-SERIES LARGE AND SMALL DROPLET FILTERS.

Applicant of the present application owns the following U.S. Provisional Patent Application, filed on Apr. 19, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. 62/659,900, titled         METHOD OF HUB COMMUNICATION.

Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Mar. 30, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U. S. Provisional Patent Application No. 62/650,898 filed on         Mar. 30, 2018, titled CAPACITIVE COUPLED RETURN PATH PAD WITH         SEPARABLE ARRAY ELEMENTS;     -   U.S. Provisional Patent Application Ser. No. 62/650,887, titled         SURGICAL SYSTEMS WITH OPTIMIZED SENSING CAPABILITIES;     -   U.S. Provisional Patent Application Ser. No. 62/650,882, titled         SMOKE EVACUATION MODULE FOR INTERACTIVE SURGICAL PLATFORM; and     -   U.S. Provisional Patent Application Ser. No. 62/650,877, titled         SURGICAL SMOKE EVACUATION SENSING AND CONTROLS.

Applicant of the present application owns the following U.S. Patent Applications, filed on Mar. 29, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. patent application Ser. No. 15/940,641, titled INTERACTIVE         SURGICAL SYSTEMS WITH ENCRYPTED COMMUNICATION CAPABILITIES;     -   U.S. patent application Ser. No. 15/940,648, titled INTERACTIVE         SURGICAL SYSTEMS WITH CONDITION HANDLING OF DEVICES AND DATA         CAPABILITIES;     -   U.S. patent application Ser. No. 15/940,656, titled SURGICAL HUB         COORDINATION OF CONTROL AND COMMUNICATION OF OPERATING ROOM         DEVICES;     -   U.S. patent application Ser. No. 15/940,666, titled SPATIAL         AWARENESS OF SURGICAL HUBS IN OPERATING ROOMS;     -   U.S. patent application Ser. No. 15/940,670, titled COOPERATIVE         UTILIZATION OF DATA DERIVED FROM SECONDARY SOURCES BY         INTELLIGENT SURGICAL HUBS;     -   U.S. patent application Ser. No. 15/940,677, titled SURGICAL HUB         CONTROL ARRANGEMENTS;     -   U.S. patent application Ser. No. 15/940,632, titled DATA         STRIPPING METHOD TO INTERROGATE PATIENT RECORDS AND CREATE         ANONYMIZED RECORD;     -   U.S. patent application Ser. No. 15/940,640, titled         COMMUNICATION HUB AND STORAGE DEVICE FOR STORING PARAMETERS AND         STATUS OF A SURGICAL DEVICE TO BE SHARED WITH CLOUD BASED         ANALYTICS SYSTEMS;     -   U.S. patent application Ser. No. 15/940,645, titled SELF         DESCRIBING DATA PACKETS GENERATED AT AN ISSUING INSTRUMENT;     -   U.S. patent application Ser. No. 15/940,649, titled DATA PAIRING         TO INTERCONNECT A DEVICE MEASURED PARAMETER WITH AN OUTCOME;     -   U.S. patent application Ser. No. 15/940,654, titled SURGICAL HUB         SITUATIONAL AWARENESS;     -   U.S. patent application Ser. No. 15/940,663, titled SURGICAL         SYSTEM DISTRIBUTED PROCESSING;     -   U.S. patent application Ser. No. 15/940,668, titled AGGREGATION         AND REPORTING OF SURGICAL HUB DATA;     -   U.S. patent application Ser. No. 15/940,671, titled SURGICAL HUB         SPATIAL AWARENESS TO DETERMINE DEVICES IN OPERATING THEATER;     -   U.S. patent application Ser. No. 15/940,686, titled DISPLAY OF         ALIGNMENT OF STAPLE CARTRIDGE TO PRIOR LINEAR STAPLE LINE;     -   U.S. patent application Ser. No. 15/940,700, titled STERILE         FIELD INTERACTIVE CONTROL DISPLAYS;     -   U.S. patent application Ser. No. 15/940,629, titled COMPUTER         IMPLEMENTED INTERACTIVE SURGICAL SYSTEMS;     -   U.S. patent application Ser. No. 15/940,704, titled USE OF LASER         LIGHT AND RED-GREEN-BLUE COLORATION TO DETERMINE PROPERTIES OF         BACK SCATTERED LIGHT;     -   U.S. patent application Ser. No. 15/940,722, titled         CHARACTERIZATION OF TISSUE IRREGULARITIES THROUGH THE USE OF         MONO-CHROMATIC LIGHT REFRACTIVITY; and     -   U.S. patent application Ser. No. 15/940,742, titled DUAL CMOS         ARRAY IMAGING.     -   U.S. patent application Ser. No. 15/940,636, titled ADAPTIVE         CONTROL PROGRAM UPDATES FOR SURGICAL DEVICES;     -   U.S. patent application Ser. No. 15/940,653, titled ADAPTIVE         CONTROL PROGRAM UPDATES FOR SURGICAL HUBS;     -   U.S. patent application Ser. No. 15/940,660, titled CLOUD-BASED         MEDICAL ANALYTICS FOR CUSTOMIZATION AND RECOMMENDATIONS TO A         USER;     -   U.S. patent application Ser. No. 15/940,679, titled CLOUD-BASED         MEDICAL ANALYTICS FOR LINKING OF LOCAL USAGE TRENDS WITH THE         RESOURCE ACQUISITION BEHAVIORS OF LARGER DATA SET;     -   U.S. patent application Ser. No. 15/940,694, titled CLOUD-BASED         MEDICAL ANALYTICS FOR MEDICAL FACILITY SEGMENTED         INDIVIDUALIZATION OF INSTRUMENT FUNCTION;     -   U.S. patent application Ser. No. 15/940,634, titled CLOUD-BASED         MEDICAL ANALYTICS FOR SECURITY AND AUTHENTICATION TRENDS AND         REACTIVE MEASURES;     -   U.S. patent application Ser. No. 15/940,706, titled DATA         HANDLING AND PRIORITIZATION IN A CLOUD ANALYTICS NETWORK; and     -   U.S. patent application Ser. No. 15/940,675, titled CLOUD         INTERFACE FOR COUPLED SURGICAL DEVICES.     -   U.S. patent application Ser. No. 15/940,627, titled DRIVE         ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,637, titled         COMMUNICATION ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL         PLATFORMS;     -   U.S. patent application Ser. No. 15/940,642, titled CONTROLS FOR         ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,676, titled AUTOMATIC         TOOL ADJUSTMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,680, titled CONTROLLERS         FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,683, titled COOPERATIVE         SURGICAL ACTIONS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. patent application Ser. No. 15/940,690, titled DISPLAY         ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS; and     -   U.S. patent application Ser. No. 15/940,711, titled SENSING         ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS.

Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Mar. 28, 2018, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. 62/649,302, titled         INTERACTIVE SURGICAL SYSTEMS WITH ENCRYPTED COMMUNICATION         CAPABILITIES;     -   U.S. Provisional Patent Application Ser. No. 62/649,294, titled         DATA STRIPPING METHOD TO INTERROGATE PATIENT RECORDS AND CREATE         ANONYMIZED RECORD;     -   U.S. Provisional Patent Application Ser. No. 62/649,300, titled         SURGICAL HUB SITUATIONAL AWARENESS;     -   U.S. Provisional Patent Application Ser. No. 62/649,309, titled         SURGICAL HUB SPATIAL AWARENESS TO DETERMINE DEVICES IN OPERATING         THEATER;     -   U.S. Provisional Patent Application Ser. No. 62/649,310, titled         COMPUTER IMPLEMENTED INTERACTIVE SURGICAL SYSTEMS;     -   U.S. Provisional Patent Application Ser. No. 62/649,291, titled         USE OF LASER LIGHT AND RED-GREEN-BLUE COLORATION TO DETERMINE         PROPERTIES OF BACK SCATTERED LIGHT;     -   U.S. Provisional Patent Application Ser. No. 62/649,296, titled         ADAPTIVE CONTROL PROGRAM UPDATES FOR SURGICAL DEVICES;     -   U.S. Provisional Patent Application Ser. No. 62/649,333, titled         CLOUD-BASED MEDICAL ANALYTICS FOR CUSTOMIZATION AND         RECOMMENDATIONS TO A USER;     -   U.S. Provisional Patent Application Ser. No. 62/649,327, titled         CLOUD-BASED MEDICAL ANALYTICS FOR SECURITY AND AUTHENTICATION         TRENDS AND REACTIVE MEASURES;     -   U.S. Provisional Patent Application Ser. No. 62/649,315, titled         DATA HANDLING AND PRIORITIZATION IN A CLOUD ANALYTICS NETWORK;     -   U.S. Provisional Patent Application Ser. No. 62/649,313, titled         CLOUD INTERFACE FOR COUPLED SURGICAL DEVICES;     -   U.S. Provisional Patent Application Ser. No. 62/649,320, titled         DRIVE ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS;     -   U.S. Provisional Patent Application Ser. No. 62/649,307, titled         AUTOMATIC TOOL ADJUSTMENTS FOR ROBOT-ASSISTED SURGICAL         PLATFORMS; and     -   U.S. Provisional Patent Application Ser. No. 62/649,323, titled         SENSING ARRANGEMENTS FOR ROBOT-ASSISTED SURGICAL PLATFORMS.

Applicant of the present application owns the following U.S. Provisional Patent Applications, filed on Dec. 28, 2017, the disclosure of each of which is herein incorporated by reference in its entirety:

-   -   U.S. Provisional Patent Application Ser. No. U.S. Provisional         Patent Application Ser. No. 62/611,341, titled INTERACTIVE         SURGICAL PLATFORM;     -   U.S. Provisional Patent Application Ser. No. 62/611,340, titled         CLOUD-BASED MEDICAL ANALYTICS; and     -   U.S. Provisional Patent Application Ser. No. 62/611,339, titled         ROBOT ASSISTED SURGICAL PLATFORM.

Before explaining various aspects of surgical devices and generators in detail, it should be noted that the illustrative examples are not limited in application or use to the details of construction and arrangement of parts illustrated in the accompanying drawings and description. The illustrative examples may be implemented or incorporated in other aspects, variations and modifications, and may be practiced or carried out in various ways. Further, unless otherwise indicated, the terms and expressions employed herein have been chosen for the purpose of describing the illustrative examples for the convenience of the reader and are not for the purpose of limitation thereof. Also, it will be appreciated that one or more of the following-described aspects, expressions of aspects, and/or examples, can be combined with any one or more of the other following-described aspects, expressions of aspects and/or examples.

Various aspects are directed to improved ultrasonic surgical devices, electrosurgical devices and generators for use therewith. Aspects of the ultrasonic surgical devices can be configured for transecting and/or coagulating tissue during surgical procedures, for example. Aspects of the electrosurgical devices can be configured for transecting, coagulating, scaling, welding and/or desiccating tissue during surgical procedures, for example.

Adaptive Ultrasonic Blade Control Algorithms

In various aspects smart ultrasonic energy devices may comprise adaptive algorithms to control the operation of the ultrasonic blade. In one aspect, the ultrasonic blade adaptive control algorithms are configured to identify tissue type and adjust device parameters. In one aspect, the ultrasonic blade control algorithms are configured to parameterize tissue type. An algorithm to detect the collagen/elastic ratio of tissue to tune the amplitude of the distal tip of the ultrasonic blade is described in the following section of the present disclosure. Various aspects of smart ultrasonic energy devices are described herein in connection with FIGS. 1-2, for example. Accordingly, the following description of adaptive ultrasonic blade control algorithms should be read in conjunction with FIGS. 1-2 and the description associated therewith.

In certain surgical procedures it would be desirable to employ adaptive ultrasonic blade control algorithms. In one aspect, adaptive ultrasonic blade control algorithms may be employed to adjust the parameters of the ultrasonic device based on the type of tissue in contact with the ultrasonic blade. In one aspect, the parameters of the ultrasonic device may be adjusted based on the location of the tissue within the jaws of the ultrasonic end effector, for example, the location of the tissue between the clamp arm and the ultrasonic blade. The impedance of the ultrasonic transducer may be employed to differentiate what percentage of the tissue is located in the distal or proximal end of the end effector. The reactions of the ultrasonic device may be based on the tissue type or compressibility of the tissue. In another aspect, the parameters of the ultrasonic device may be adjusted based on the identified tissue type or parameterization. For example, the mechanical displacement amplitude of the distal tip of the ultrasonic blade may be tuned based on the ration of collagen to elastin tissue detected during the tissue identification procedure. The ratio of collagen to elastin tissue may be detected used a variety of techniques including infrared (IR) surface reflectance and emissivity. The force applied to the tissue by the clamp arm and/or the stroke of the clamp arm to produce gap and compression. Electrical continuity across a jaw equipped with electrodes may be employed to determine what percentage of the jaw is covered with tissue.

FIG. 1 is a system 800 configured to execute adaptive ultrasonic blade control algorithms in a surgical data network comprising a modular communication hub, in accordance with at least one aspect of the present disclosure. In one aspect, the generator module 240 is configured to execute the adaptive ultrasonic blade control algorithm(s) 802 as described herein. In another aspect, the device/instrument 235 is configured to execute the adaptive ultrasonic blade control algorithm(s) 804 as described herein with reference to FIGS. 19-32. In another aspect, both the device/instrument 235 and the device/instrument 235 are configured to execute the adaptive ultrasonic blade control algorithms 802, 804 as described herein with reference to FIGS. 19-32.

The generator module 240 may comprise a patient isolated stage in communication with a non-isolated stage via a power transformer. A secondary winding of the power transformer is contained in the isolated stage and may comprise a tapped configuration (e.g., a center-tapped or a non-center-tapped configuration) to define drive signal outputs for delivering drive signals to different surgical instruments, such as, for example, an ultrasonic surgical instrument, an RF electrosurgical instrument, and a multifunction surgical instrument which includes ultrasonic and RF energy modes that can be delivered alone or simultaneously. In particular, the drive signal outputs may output an ultrasonic drive signal (e.g., a 420V root-mean-square (RMS) drive signal) to an ultrasonic surgical instrument 241, and the drive signal outputs may output an RF electrosurgical drive signal (e.g., a 100V RMS drive signal) to an RF electrosurgical instrument 241. Aspects of the generator module 240 are described herein with reference to FIGS. 7-12.

The generator module 240 or the device/instrument 235 or both are coupled to the modular control tower 236 connected to multiple operating theater devices such as, for example, intelligent surgical instruments, robots, and other computerized devices located in the operating theater. In some aspects, a surgical data network may include a modular communication hub configured to connect modular devices located in one or more operating theaters of a healthcare facility, or any room in a healthcare facility specially equipped for surgical operations, to a cloud-based system (e.g., the cloud 204 that may include a remote server 213 coupled to a storage device).

Modular devices located in the operating theater may be coupled to the modular communication hub. The network hub and/or the network switch may be coupled to a network router to connect the devices to the cloud 204 or a local computer system. Data associated with the devices may be transferred to cloud-based computers via the router for remote data processing and manipulation. Data associated with the devices may also be transferred to a local computer system for local data processing and manipulation. Modular devices located in the same operating theater also may be coupled to a network switch. The network switch may be coupled to the network hub and/or the network router to connect to the devices to the cloud 204. Data associated with the devices may be transferred to the cloud 204 via the network router for data processing and manipulation. Data associated with the devices may also be transferred to the local computer system for local data processing and manipulation.

It will be appreciated that cloud computing relies on sharing computing resources rather than having local servers or personal devices to handle software applications. The word “cloud” may be used as a metaphor for “the Internet,” although the term is not limited as such. Accordingly, the term “cloud computing” may be used herein to refer to “a type of Internet-based computing,” where different services—such as servers, storage, and applications—are delivered to the modular communication hub and/or computer system located in the surgical theater (e.g., a fixed, mobile, temporary, or field operating room or space) and to devices connected to the modular communication hub and/or computer system through the Internet. The cloud infrastructure may be maintained by a cloud service provider. In this context, the cloud service provider may be the entity that coordinates the usage and control of the devices located in one or more operating theaters. The cloud computing services can perform a large number of calculations based on the data gathered by smart surgical instruments, robots, and other computerized devices located in the operating theater. The hub hardware enables multiple devices or connections to be connected to a computer that communicates with the cloud computing resources and storage.

FIG. 1 further illustrates some aspects of a computer-implemented interactive surgical system comprising a modular communication hub that may include the system 800 configured to execute adaptive ultrasonic blade control algorithms in a surgical data network. The surgical system may include at least one surgical hub in communication with a cloud 204 that may include a remote server 213. In one aspect, the computer-implemented interactive surgical system comprises a modular control tower 236 connected to multiple operating theater devices such as, for example, intelligent surgical instruments, robots, and other computerized devices located in the operating theater. The modular control tower 236 may comprise a modular communication hub coupled to a computer system. In some aspects, the modular control tower 236 is coupled to an imaging module that is coupled to an endoscope, a generator module 240 that is coupled to an energy device 241, and a smart device/instrument 235 optionally coupled to a display 237. The operating theater devices are coupled to cloud computing resources and data storage via the modular control tower 236. A robot hub 222 also may be connected to the modular control tower 236 and to the cloud computing resources. The devices/instruments 235, visualization systems 208, among others, may be coupled to the modular control tower 236 via wired or wireless communication standards or protocols, as described herein. The modular control tower 236 may be coupled to a hub display 215 (e.g., monitor, screen) to display and overlay images received from the imaging module, device/instrument display, and/or other visualization systems 208. The hub display 215 also may display data received from devices connected to the modular control tower in conjunction with images and overlaid images.

Generator Hardware

FIG. 2 illustrates an example of a generator 900, which is one form of a generator configured to couple to an ultrasonic instrument and further configured to execute adaptive ultrasonic blade control algorithms in a surgical data network comprising a modular communication hub as shown in FIG. 1. The generator 900 is configured to deliver multiple energy modalities to a surgical instrument. The generator 900 provides RF and ultrasonic signals for delivering energy to a surgical instrument either independently or simultaneously. The RF and ultrasonic signals may be provided alone or in combination and may be provided simultaneously. As noted above, at least one generator output can deliver multiple energy modalities (e.g., ultrasonic, bipolar or monopolar RF, irreversible and/or reversible electroporation, and/or microwave energy, among others) through a single port, and these signals can be delivered separately or simultaneously to the end effector to treat tissue. The generator 900 comprises a processor 902 coupled to a waveform generator 904. The processor 902 and waveform generator 904 are configured to generate a variety of signal waveforms based on information stored in a memory coupled to the processor 902, not shown for clarity of disclosure. The digital information associated with a waveform is provided to the waveform generator 904 which includes one or more DAC circuits to convert the digital input into an analog output. The analog output is fed to an amplifier 906 for signal conditioning and amplification. The conditioned and amplified output of the amplifier 906 is coupled to a power transformer 908. The signals are coupled across the power transformer 908 to the secondary side, which is in the patient isolation side. A first signal of a first energy modality is provided to the surgical instrument between the terminals labeled ENERGY₁ and RETURN. A second signal of a second energy modality is coupled across a capacitor 910 and is provided to the surgical instrument between the terminals labeled ENERGY₂ and RETURN. It will be appreciated that more than two energy modalities may be output and thus the subscript “n” may be used to designate that up to n ENERGY_(n) terminals may be provided, where n is a positive integer greater than 1. It also will be appreciated that up to “n” return paths RETURN_(n) may be provided without departing from the scope of the present disclosure.

A first voltage sensing circuit 912 is coupled across the terminals labeled ENERGY₁ and the RETURN path to measure the output voltage therebetween. A second voltage sensing circuit 924 is coupled across the terminals labeled ENERGY₂ and the RETURN path to measure the output voltage therebetween. A current sensing circuit 914 is disposed in series with the RETURN leg of the secondary side of the power transformer 908 as shown to measure the output current for either energy modality. If different return paths are provided for each energy modality, then a separate current sensing circuit should be provided in each return leg. The outputs of the first and second voltage sensing circuits 912, 924 are provided to respective isolation transformers 916, 922 and the output of the current sensing circuit 914 is provided to another isolation transformer 918. The outputs of the isolation transformers 916, 928, 922 in the on the primary side of the power transformer 908 (non-patient isolated side) are provided to a one or more ADC circuit 926. The digitized output of the ADC circuit 926 is provided to the processor 902 for further processing and computation. The output voltages and output current feedback information can be employed to adjust the output voltage and current provided to the surgical instrument and to compute output impedance, among other parameters. Input/output communications between the processor 902 and patient isolated circuits is provided through an interface circuit 920. Sensors also may be in electrical communication with the processor 902 by way of the interface circuit 920.

In one aspect, the impedance may be determined by the processor 902 by dividing the output of either the first voltage sensing circuit 912 coupled across the terminals labeled ENERGY₁/RETURN or the second voltage sensing circuit 924 coupled across the terminals labeled ENERGY₂/RETURN by the output of the current sensing circuit 914 disposed in series with the RETURN leg of the secondary side of the power transformer 908. The outputs of the first and second voltage sensing circuits 912, 924 are provided to separate isolations transformers 916, 922 and the output of the current sensing circuit 914 is provided to another isolation transformer 916. The digitized voltage and current sensing measurements from the ADC circuit 926 are provided the processor 902 for computing impedance. As an example, the first energy modality ENERGY₁ may be ultrasonic energy and the second energy modality ENERGY₂ may be RF energy. Nevertheless, in addition to ultrasonic and bipolar or monopolar RF energy modalities, other energy modalities include irreversible and/or reversible electroporation and/or microwave energy, among others. Also, although the example illustrated in FIG. 2 shows a single return path RETURN may be provided for two or more energy modalities, in other aspects, multiple return paths RETURN_(n) may be provided for each energy modality ENERGY_(n). Thus, as described herein, the ultrasonic transducer impedance may be measured by dividing the output of the first voltage sensing circuit 912 by the current sensing circuit 914 and the tissue impedance may be measured by dividing the output of the second voltage sensing circuit 924 by the current sensing circuit 914.

As shown in FIG. 2, the generator 900 comprising at least one output port can include a power transformer 908 with a single output and with multiple taps to provide power in the form of one or more energy modalities, such as ultrasonic, bipolar or monopolar RF, irreversible and/or reversible electroporation, and/or microwave energy, among others, for example, to the end effector depending on the type of treatment of tissue being performed. For example, the generator 900 can deliver energy with higher voltage and lower current to drive an ultrasonic transducer, with lower voltage and higher current to drive RF electrodes for sealing tissue, or with a coagulation waveform for spot coagulation using either monopolar or bipolar RF electrosurgical electrodes. The output waveform from the generator 900 can be steered, switched, or filtered to provide the frequency to the end effector of the surgical instrument. The connection of an ultrasonic transducer to the generator 900 output would be preferably located between the output labeled ENERGY₁ and RETURN as shown in FIG. 2. In one example, a connection of RF bipolar electrodes to the generator 900 output would be preferably located between the output labeled ENERGY₂ and RETURN. In the case of monopolar output, the preferred connections would be active electrode (e.g., pencil or other probe) to the ENERGY₂ output and a suitable return pad connected to the RETURN output.

Additional details are disclosed in U.S. Patent Application Publication No. 2017/0086914, titled TECHNIQUES FOR OPERATING GENERATOR FOR DIGITALLY GENERATING ELECTRICAL SIGNAL WAVEFORMS AND SURGICAL INSTRUMENTS, which published on Mar. 30, 2017, which is herein incorporated by reference in its entirety.

As used throughout this description, the term “wireless” and its derivatives may be used to describe circuits, devices, systems, methods, techniques, communications channels, etc., that may communicate data through the use of modulated electromagnetic radiation through a non-solid medium. The term does not imply that the associated devices do not contain any wires, although in some aspects they might not. The communication module may implement any of a number of wireless or wired communication standards or protocols, including but not limited to W-Fi (IEEE 802.11 family), WiMAX (IEEE 802.16 family), IEEE 802.20, long term evolution (LTE), Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT, Bluetooth, Ethernet derivatives thereof, as well as any other wireless and wired protocols that are designated as 3G, 4G, 5G, and beyond. The computing module may include a plurality of communication modules. For instance, a first communication module may be dedicated to shorter range wireless communications such as Wi-Fi and Bluetooth and a second communication module may be dedicated to longer range wireless communications such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, and others.

As used herein a processor or processing unit is an electronic circuit which performs operations on some external data source, usually memory or some other data stream. The term is used herein to refer to the central processor (central processing unit) in a system or computer systems (especially systems on a chip (SoCs)) that combine a number of specialized “processors.”

As used herein, a system on a chip or system on chip (SoC or SOC) is an integrated circuit (also known as an “IC” or “chip”) that integrates all components of a computer or other electronic systems. It may contain digital, analog, mixed-signal, and often radio-frequency functions—all on a single substrate. A SoC integrates a microcontroller (or microprocessor) with advanced peripherals like graphics processing unit (GPU), W-Fi module, or coprocessor. A SoC may or may not contain built-in memory.

As used herein, a microcontroller or controller is a system that integrates a microprocessor with peripheral circuits and memory. A microcontroller (or MCU for microcontroller unit) may be implemented as a small computer on a single integrated circuit. It may be similar to a SoC; an SoC may include a microcontroller as one of its components. A microcontroller may contain one or more core processing units (CPUs) along with memory and programmable input/output peripherals. Program memory in the form of Ferroelectric RAM, NOR flash or OTP ROM is also often included on chip, as well as a small amount of RAM. Microcontrollers may be employed for embedded applications, in contrast to the microprocessors used in personal computers or other general purpose applications consisting of various discrete chips.

As used herein, the term controller or microcontroller may be a stand-alone IC or chip device that interfaces with a peripheral device. This may be a link between two parts of a computer or a controller on an external device that manages the operation of (and connection with) that device.

Any of the processors or microcontrollers described herein, may be implemented by any single core or multicore processor such as those known under the trade name ARM Cortex by Texas Instruments. In one aspect, the processor may be an LM4F230H5QR ARM Cortex-M4F Processor Core, available from Texas Instruments, for example, comprising on-chip memory of 256 KB single-cycle flash memory, or other non-volatile memory, up to 40 MHz, a prefetch buffer to improve performance above 40 MHz, a 32 KB single-cycle serial random access memory (SRAM), internal read-only memory (ROM) loaded with StellarisWare® software, 2 KB electrically erasable programmable read-only memory (EEPROM), one or more pulse width modulation (PWM) modules, one or more quadrature encoder inputs (QEI) analog, one or more 12-bit Analog-to-Digital Converters (ADC) with 12 analog input channels, details of which are available for the product datasheet.

In one aspect, the processor may comprise a safety controller comprising two controller-based families such as TMS570 and RM4x known under the trade name Hercules ARM Cortex R4, also by Texas Instruments. The safety controller may be configured specifically for IEC 61508 and ISO 26262 safety critical applications, among others, to provide advanced integrated safety features while delivering scalable performance, connectivity, and memory options.

Modular devices include the modules (as described in connection with FIG. 3, for example) that are receivable within a surgical hub and the surgical devices or instruments that can be connected to the various modules in order to connect or pair with the corresponding surgical hub. The modular devices include, for example, intelligent surgical instruments, medical imaging devices, suction/irrigation devices, smoke evacuators, energy generators, ventilators, insufflators, and displays. The modular devices described herein can be controlled by control algorithms. The control algorithms can be executed on the modular device itself, on the surgical hub to which the particular modular device is paired, or on both the modular device and the surgical hub (e.g., via a distributed computing architecture). In some exemplifications, the modular devices' control algorithms control the devices based on data sensed by the modular device itself (i.e., by sensors in, on, or connected to the modular device). This data can be related to the patient being operated on (e.g., tissue properties or insufflation pressure) or the modular device itself (e.g., the rate at which a knife is being advanced, motor current, or energy levels). For example, a control algorithm for a surgical stapling and cutting instrument can control the rate at which the instrument's motor drives its knife through tissue according to resistance encountered by the knife as it advances.

FIG. 3 illustrates one form of a surgical system 1000 comprising a generator 1100 and various surgical instruments 1104, 1106, 1108 usable therewith, where the surgical instrument 1104 is an ultrasonic surgical instrument, the surgical instrument 1106 is an RF electrosurgical instrument, and the multifunction surgical instrument 1108 is a combination ultrasonic/RF electrosurgical instrument. The generator 1100 is configurable for use with a variety of surgical instruments. According to various forms, the generator 1100 may be configurable for use with different surgical instruments of different types including, for example, ultrasonic surgical instruments 1104, RF electrosurgical instruments 1106, and multifunction surgical instruments 1108 that integrate RF and ultrasonic energies delivered simultaneously from the generator 1100. Although in the form of FIG. 3 the generator 1100 is shown separate from the surgical instruments 1104, 1106, 1108 in one form, the generator 1100 may be formed integrally with any of the surgical instruments 1104, 1106, 1108 to form a unitary surgical system. The generator 1100 comprises an input device 1110 located on a front panel of the generator 1100 console. The input device 1110 may comprise any suitable device that generates signals suitable for programming the operation of the generator 1100. The generator 1100 may be configured for wired or wireless communication.

The generator 1100 is configured to drive multiple surgical instruments 1104, 1106, 1108. The first surgical instrument is an ultrasonic surgical instrument 1104 and comprises a handpiece 1105 (HP), an ultrasonic transducer 1120, a shaft 1126, and an end effector 1122. The end effector 1122 comprises an ultrasonic blade 1128 acoustically coupled to the ultrasonic transducer 1120 and a clamp arm 1140. The handpiece 1105 comprises a trigger 1143 to operate the clamp arm 1140 and a combination of the toggle buttons 1134 a, 1134 b, 1134 c to energize and drive the ultrasonic blade 1128 or other function. The toggle buttons 1134 a, 1134 b, 1134 c can be configured to energize the ultrasonic transducer 1120 with the generator 1100.

The generator 1100 also is configured to drive a second surgical instrument 1106. The second surgical instrument 1106 is an RF electrosurgical instrument and comprises a handpiece 1107 (HP), a shaft 1127, and an end effector 1124. The end effector 1124 comprises electrodes in clamp arms 1142 a, 1142 b and return through an electrical conductor portion of the shaft 1127. The electrodes are coupled to and energized by a bipolar energy source within the generator 1100. The handpiece 1107 comprises a trigger 1145 to operate the clamp arms 1142 a, 1142 b and an energy button 1135 to actuate an energy switch to energize the electrodes in the end effector 1124.

The generator 1100 also is configured to drive a multifunction surgical instrument 1108. The multifunction surgical instrument 1108 comprises a handpiece 1109 (HP), a shaft 1129, and an end effector 1125. The end effector 1125 comprises an ultrasonic blade 1149 and a clamp arm 1146. The ultrasonic blade 1149 is acoustically coupled to the ultrasonic transducer 1120. The handpiece 1109 comprises a trigger 1147 to operate the clamp arm 1146 and a combination of the toggle buttons 1137 a, 1137 b, 1137 c to energize and drive the ultrasonic blade 1149 or other function. The toggle buttons 1137 a, 1137 b, 1137 c can be configured to energize the ultrasonic transducer 1120 with the generator 1100 and energize the ultrasonic blade 1149 with a bipolar energy source also contained within the generator 1100.

The generator 1100 is configurable for use with a variety of surgical instruments. According to various forms, the generator 1100 may be configurable for use with different surgical instruments of different types including, for example, the ultrasonic surgical instrument 1104, the RF electrosurgical instrument 1106, and the multifunction surgical instrument 1108 that integrates RF and ultrasonic energies delivered simultaneously from the generator 1100. Although in the form of FIG. 3 the generator 1100 is shown separate from the surgical instruments 1104, 1106, 1108, in another form the generator 1100 may be formed integrally with any one of the surgical instruments 1104, 1106, 1108 to form a unitary surgical system. As discussed above, the generator 1100 comprises an input device 1110 located on a front panel of the generator 1100 console. The input device 1110 may comprise any suitable device that generates signals suitable for programming the operation of the generator 1100. The generator 1100 also may comprise one or more output devices 1112. Further aspects of generators for digitally generating electrical signal waveforms and surgical instruments are described in US patent publication US-2017-0086914-A1, which is herein incorporated by reference in its entirety.

FIG. 4 is an end effector 1122 of the example ultrasonic device 1104, in accordance with at least one aspect of the present disclosure. The end effector 1122 may comprise a blade 1128 that may be coupled to the ultrasonic transducer 1120 via a wave guide. When driven by the ultrasonic transducer 1120, the blade 1128 may vibrate and, when brought into contact with tissue, may cut and/or coagulate the tissue, as described herein. According to various aspects, and as illustrated in FIG. 4, the end effector 1122 may also comprise a clamp arm 1140 that may be configured for cooperative action with the blade 1128 of the end effector 1122. With the blade 1128, the clamp arm 1140 may comprise a set of jaws. The clamp arm 1140 may be pivotally connected at a distal end of a shaft 1126 of the instrument portion 1104. The clamp arm 1140 may include a clamp arm tissue pad 1163, which may be formed from TEFLON® or other suitable low-friction material. The pad 1163 may be mounted for cooperation with the blade 1128, with pivotal movement of the clamp arm 1140 positioning the clamp pad 1163 in substantially parallel relationship to, and in contact with, the blade 1128. By this construction, a tissue bite to be clamped may be grasped between the tissue pad 1163 and the blade 1128. The tissue pad 1163 may be provided with a sawtooth-like configuration including a plurality of axially spaced, proximally extending gripping teeth 1161 to enhance the gripping of tissue in cooperation with the blade 1128. The clamp arm 1140 may transition from the open position shown in FIG. 4 to a closed position (with the clamp arm 1140 in contact with or proximity to the blade 1128) in any suitable manner. For example, the handpiece 1105 may comprise a jaw closure trigger. When actuated by a clinician, the jaw closure trigger may pivot the clamp arm 1140 in any suitable manner.

The generator 1100 may be activated to provide the drive signal to the ultrasonic transducer 1120 in any suitable manner. For example, the generator 1100 may comprise a foot switch 1430 (FIG. 5) coupled to the generator 1100 via a footswitch cable 1432. A clinician may activate the ultrasonic transducer 1120, and thereby the ultrasonic transducer 1120 and blade 1128, by depressing the foot switch 1430. In addition, or instead of the foot switch 1430, some aspects of the device 1104 may utilize one or more switches positioned on the handpiece 1105 that, when activated, may cause the generator 1100 to activate the ultrasonic transducer 1120. In one aspect, for example, the one or more switches may comprise a pair of toggle buttons 1134, 1134 a, 1134 b (FIG. 3), for example, to determine an operating mode of the device 1104. When the toggle button 1134 a is depressed, for example, the ultrasonic generator 1100 may provide a maximum drive signal to the ultrasonic transducer 1120, causing it to produce maximum ultrasonic energy output. Depressing toggle button 1134 b may cause the ultrasonic generator 1100 to provide a user-selectable drive signal to the ultrasonic transducer 1120, causing it to produce less than the maximum ultrasonic energy output. The device 1104 additionally or alternatively may comprise a second switch to, for example, indicate a position of a jaw closure trigger for operating the jaws via the clamp arm 1140 of the end effector 1122. Also, in some aspects, the ultrasonic generator 1100 may be activated based on the position of the jaw closure trigger, (e.g., as the clinician depresses the jaw closure trigger to close the jaws via the clamp arm 1140, ultrasonic energy may be applied).

Additionally or alternatively, the one or more switches may comprise a toggle button 1134 that, when depressed, causes the generator 1100 to provide a pulsed output (FIG. 3). The pulses may be provided at any suitable frequency and grouping, for example. In certain aspects, the power level of the pulses may be the power levels associated with toggle buttons 1134 a, 1134 b (maximum, less than maximum), for example.

It will be appreciated that a device 1104 may comprise any combination of the toggle buttons 1134 a, 1134 b, 1134 (FIG. 3). For example, the device 1104 could be configured to have only two toggle buttons: a toggle button 1134 a for producing maximum ultrasonic energy output and a toggle button 1134 for producing a pulsed output at either the maximum or less than maximum power level per. In this way, the drive signal output configuration of the generator 1100 could be five continuous signals, or any discrete number of individual pulsed signals (1, 2, 3, 4, or 5). In certain aspects, the specific drive signal configuration may be controlled based upon, for example, EEPROM settings in the generator 1100 and/or user power level selection(s).

In certain aspects, a two-position switch may be provided as an alternative to a toggle button 1134 (FIG. 3). For example, a device 1104 may include a toggle button 1134 a for producing a continuous output at a maximum power level and a two-position toggle button 1134 b. In a first detented position, toggle button 1134 b may produce a continuous output at a less than maximum power level, and in a second detented position the toggle button 1134 b may produce a pulsed output (e.g., at either a maximum or less than maximum power level, depending upon the EEPROM settings).

In some aspects, the RF electrosurgical end effector 1124, 1125 (FIG. 3) may also comprise a pair of electrodes. The electrodes may be in communication with the generator 1100, for example, via a cable. The electrodes may be used, for example, to measure an impedance of a tissue bite present between the clamp arm 1142 a, 1146 and the blade 1142 b, 1149. The generator 1100 may provide a signal (e.g., a non-therapeutic signal) to the electrodes. The impedance of the tissue bite may be found, for example, by monitoring the current, voltage, etc. of the signal.

In various aspects, the generator 1100 may comprise several separate functional elements, such as modules and/or blocks, as shown in FIG. 5, a diagram of the surgical system 1000 of FIG. 3. Different functional elements or modules may be configured for driving the different kinds of surgical devices 1104, 1106, 1108. For example an ultrasonic generator module may drive an ultrasonic device, such as the ultrasonic device 1104. An electrosurgery/RF generator module may drive the electrosurgical device 1106. The modules may generate respective drive signals for driving the surgical devices 1104, 1106, 1108. In various aspects, the ultrasonic generator module and/or the electrosurgery/RF generator module each may be formed integrally with the generator 1100. Alternatively, one or more of the modules may be provided as a separate circuit module electrically coupled to the generator 1100. (The modules are shown in phantom to illustrate this option.) Also, in some aspects, the electrosurgery/RF generator module may be formed integrally with the ultrasonic generator module, or vice versa.

In accordance with the described aspects, the ultrasonic generator module may produce a drive signal or signals of particular voltages, currents, and frequencies (e.g. 55,500 cycles per second, or Hz). The drive signal or signals may be provided to the ultrasonic device 1104, and specifically to the transducer 1120, which may operate, for example, as described above. In one aspect, the generator 1100 may be configured to produce a drive signal of a particular voltage, current, and/or frequency output signal that can be stepped with high resolution, accuracy, and repeatability.

In accordance with the described aspects, the electrosurgery/RF generator module may generate a drive signal or signals with output power sufficient to perform bipolar electrosurgery using radio frequency (RF) energy. In bipolar electrosurgery applications, the drive signal may be provided, for example, to the electrodes of the electrosurgical device 1106, for example, as described above. Accordingly, the generator 1100 may be configured for therapeutic purposes by applying electrical energy to the tissue sufficient for treating the tissue (e.g., coagulation, cauterization, tissue welding, etc.).

The generator 1100 may comprise an input device 2150 (FIG. 8B) located, for example, on a front panel of the generator 1100 console. The input device 2150 may comprise any suitable device that generates signals suitable for programming the operation of the generator 1100. In operation, the user can program or otherwise control operation of the generator 1100 using the input device 2150. The input device 2150 may comprise any suitable device that generates signals that can be used by the generator (e.g., by one or more processors contained in the generator) to control the operation of the generator 1100 (e.g., operation of the ultrasonic generator module and/or electrosurgery/RF generator module). In various aspects, the input device 2150 includes one or more of: buttons, switches, thumbwheels, keyboard, keypad, touch screen monitor, pointing device, remote connection to a general purpose or dedicated computer. In other aspects, the input device 2150 may comprise a suitable user interface, such as one or more user interface screens displayed on a touch screen monitor, for example. Accordingly, by way of the input device 2150, the user can set or program various operating parameters of the generator, such as, for example, current (I), voltage (V), frequency (f), and/or period (T) of a drive signal or signals generated by the ultrasonic generator module and/or electrosurgery/RF generator module.

The generator 1100 may also comprise an output device 2140 (FIG. 8B) located, for example, on a front panel of the generator 1100 console. The output device 2140 includes one or more devices for providing a sensory feedback to a user. Such devices may comprise, for example, visual feedback devices (e.g., an LCD display screen, LED indicators), audio feedback devices (e.g., a speaker, a buzzer) or tactile feedback devices (e.g., haptic actuators).

Although certain modules and/or blocks of the generator 1100 may be described by way of example, it can be appreciated that a greater or lesser number of modules and/or blocks may be used and still fall within the scope of the aspects. Further, although various aspects may be described in terms of modules and/or blocks to facilitate description, such modules and/or blocks may be implemented by one or more hardware components, e.g., processors, Digital Signal Processors (DSPs), Programmable Logic Devices (PLDs), Application Specific Integrated Circuits (ASICs), circuits, registers and/or software components, e.g., programs, subroutines, logic and/or combinations of hardware and software components.

In one aspect, the ultrasonic generator drive module and electrosurgery/RF drive module 1110 (FIG. 3) may comprise one or more embedded applications implemented as firmware, software, hardware, or any combination thereof. The modules may comprise various executable modules such as software, programs, data, drivers, application program interfaces (APIs), and so forth. The firmware may be stored in nonvolatile memory (NVM), such as in bit-masked read-only memory (ROM) or flash memory. In various implementations, storing the firmware in ROM may preserve flash memory. The NVM may comprise other types of memory including, for example, programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or battery backed random-access memory (RAM) such as dynamic RAM (DRAM), Double-Data-Rate DRAM (DDRAM), and/or synchronous DRAM (SDRAM).

In one aspect, the modules comprise a hardware component implemented as a processor for executing program instructions for monitoring various measurable characteristics of the devices 1104, 1106, 1108 and generating a corresponding output drive signal or signals for operating the devices 1104, 1106, 1108. In aspects in which the generator 1100 is used in conjunction with the device 1104, the drive signal may drive the ultrasonic transducer 1120 in cutting and/or coagulation operating modes. Electrical characteristics of the device 1104 and/or tissue may be measured and used to control operational aspects of the generator 1100 and/or provided as feedback to the user. In aspects in which the generator 1100 is used in conjunction with the device 1106, the drive signal may supply electrical energy (e.g., RF energy) to the end effector 1124 in cutting, coagulation and/or desiccation modes. Electrical characteristics of the device 1106 and/or tissue may be measured and used to control operational aspects of the generator 1100 and/or provided as feedback to the user. In various aspects, as previously discussed, the hardware components may be implemented as DSP, PLD, ASIC, circuits, and/or registers. In one aspect, the processor may be configured to store and execute computer software program instructions to generate the step function output signals for driving various components of the devices 1104, 1106, 1108, such as the ultrasonic transducer 1120 and the end effectors 1122, 1124, 1125.

An electromechanical ultrasonic system includes an ultrasonic transducer, a waveguide, and an ultrasonic blade. The electromechanical ultrasonic system has an initial resonant frequency defined by the physical properties of the ultrasonic transducer, the waveguide, and the ultrasonic blade. The ultrasonic transducer is excited by an alternating voltage V_(g)(t) and current I_(g)(t) signal equal to the resonant frequency of the electromechanical ultrasonic system. When the electromechanical ultrasonic system is at resonance, the phase difference between the voltage V_(g)(t) and current I_(g)(t) signals is zero. Stated another way, at resonance the inductive impedance is equal to the capacitive impedance. As the ultrasonic blade heats up, the compliance of the ultrasonic blade (modeled as an equivalent capacitance) causes the resonant frequency of the electromechanical ultrasonic system to shift. Thus, the inductive impedance is no longer equal to the capacitive impedance causing a mismatch between the drive frequency and the resonant frequency of the electromechanical ultrasonic system. The system is now operating “off-resonance.” The mismatch between the drive frequency and the resonant frequency is manifested as a phase difference between the voltage V_(g)(t) and current I_(g)(t) signals applied to the ultrasonic transducer. The generator electronics can easily monitor the phase difference between the voltage V_(g)(t) and current I_(g)(t) signals and can continuously adjust the drive frequency until the phase difference is once again zero. At this point, the new drive frequency is equal to the new resonant frequency of the electromechanical ultrasonic system. The change in phase and/or frequency can be used as an indirect measurement of the ultrasonic blade temperature.

As shown in FIG. 6, the electromechanical properties of the ultrasonic transducer may be modeled as an equivalent circuit comprising a first branch having a static capacitance and a second “motional” branch having a serially connected inductance, resistance and capacitance that define the electromechanical properties of a resonator. Known ultrasonic generators may include a tuning inductor for tuning out the static capacitance at a resonant frequency so that substantially all of generator's drive signal current flows into the motional branch. Accordingly, by using a tuning inductor, the generator's drive signal current represents the motional branch current, and the generator is thus able to control its drive signal to maintain the ultrasonic transducer's resonant frequency. The tuning inductor may also transform the phase impedance plot of the ultrasonic transducer to improve the generator's frequency lock capabilities. However, the tuning inductor must be matched with the specific static capacitance of an ultrasonic transducer at the operational resonance frequency. In other words, a different ultrasonic transducer having a different static capacitance requires a different tuning inductor.

FIG. 6 illustrates an equivalent circuit 1500 of an ultrasonic transducer, such as the ultrasonic transducer 1120, according to one aspect. The circuit 1500 comprises a first “motional” branch having a serially connected inductance L_(s), resistance R_(s) and capacitance C_(s) that define the electromechanical properties of the resonator, and a second capacitive branch having a static capacitance C₀. Drive current I_(g)(t) may be received from a generator at a drive voltage V_(g)(t), with motional current I_(m)(t) flowing through the first branch and current I_(g)(t)-I_(m)(t) flowing through the capacitive branch. Control of the electromechanical properties of the ultrasonic transducer may be achieved by suitably controlling I_(g)(t) and V_(g)(t). As explained above, known generator architectures may include a tuning inductor L_(t) (shown in phantom in FIG. 6) in a parallel resonance circuit for tuning out the static capacitance C₀ at a resonant frequency so that substantially all of the generator's current output I_(g)(t) flows through the motional branch. In this way, control of the motional branch current I_(m)(t) is achieved by controlling the generator current output I_(g)(t). The tuning inductor L_(t) is specific to the static capacitance C₀ of an ultrasonic transducer, however, and a different ultrasonic transducer having a different static capacitance requires a different tuning inductor L_(t). Moreover, because the tuning inductor L_(t) is matched to the nominal value of the static capacitance C₀ at a single resonant frequency, accurate control of the motional branch current I_(m)(t) is assured only at that frequency. As frequency shifts down with transducer temperature, accurate control of the motional branch current is compromised.

Various aspects of the generator 1100 may not rely on a tuning inductor L_(t) to monitor the motional branch current I_(m)(t). Instead, the generator 1100 may use the measured value of the static capacitance C₀ in between applications of power for a specific ultrasonic surgical device 1104 (along with drive signal voltage and current feedback data) to determine values of the motional branch current I_(m)(t) on a dynamic and ongoing basis (e.g., in real-time). Such aspects of the generator 1100 are therefore able to provide virtual tuning to simulate a system that is tuned or resonant with any value of static capacitance C₀ at any frequency, and not just at a single resonant frequency dictated by a nominal value of the static capacitance C₀.

FIG. 7 is a simplified block diagram of one aspect of the generator 1100 for providing inductorless tuning as described above, among other benefits. FIGS. 8A-8C illustrate an architecture of the generator 1100 of FIG. 7 according to one aspect. With reference to FIG. 7, the generator 1100 may comprise a patient isolated stage 1520 in communication with a non-isolated stage 1540 via a power transformer 1560. A secondary winding 1580 of the power transformer 1560 is contained in the isolated stage 1520 and may comprise a tapped configuration (e.g., a center-tapped or non-center tapped configuration) to define drive signal outputs 1600 a, 1600 b, 1600 c for outputting drive signals to different surgical devices, such as, for example, an ultrasonic surgical device 1104 and an electrosurgical device 1106. In particular, drive signal outputs 1600 a, 1600 b, 1600 c may output a drive signal (e.g., a 420V RMS drive signal) to an ultrasonic surgical device 1104, and drive signal outputs 1600 a, 1600 b, 1600 c may output a drive signal (e.g., a 100V RMS drive signal) to an electrosurgical device 1106, with output 1600 b corresponding to the center tap of the power transformer 1560. The non-isolated stage 1540 may comprise a power amplifier 1620 having an output connected to a primary winding 1640 of the power transformer 1560. In certain aspects the power amplifier 1620 may comprise a push-pull amplifier, for example. The non-isolated stage 1540 may further comprise a programmable logic device 1660 for supplying a digital output to a digital-to-analog converter (DAC) 1680, which in turn supplies a corresponding analog signal to an input of the power amplifier 1620. In certain aspects the programmable logic device 1660 may comprise a field-programmable gate array (FPGA), for example. The programmable logic device 1660, by virtue of controlling the power amplifier's 1620 input via the DAC 1680, may therefore control any of a number of parameters (e.g., frequency, waveform shape, waveform amplitude) of drive signals appearing at the drive signal outputs 1600 a, 1600 b, 1600 c. In certain aspects and as discussed below, the programmable logic device 1660, in conjunction with a processor (e.g., processor 1740 discussed below), may implement a number of digital signal processing (DSP)-based and/or other control algorithms to control parameters of the drive signals output by the generator 1100.

Power may be supplied to a power rail of the power amplifier 1620 by a switch-mode regulator 1700. In certain aspects the switch-mode regulator 1700 may comprise an adjustable buck regulator 1170, for example. As discussed above, the non-isolated stage 1540 may further comprise a processor 1740, which in one aspect may comprise a DSP processor such as an ADSP-21469 SHARC DSP, available from Analog Devices, Norwood, Mass., for example. In certain aspects the processor 1740 may control operation of the switch-mode power converter 1700 responsive to voltage feedback data received from the power amplifier 1620 by the processor 1740 via an analog-to-digital converter (ADC) 1760. In one aspect, for example, the processor 1740 may receive as input, via the ADC 1760, the waveform envelope of a signal (e.g., an RF signal) being amplified by the power amplifier 1620. The processor 1740 may then control the switch-mode regulator 1700 (e.g., via a pulse-width modulated (PWM) output) such that the rail voltage supplied to the power amplifier 1620 tracks the waveform envelope of the amplified signal. By dynamically modulating the rail voltage of the power amplifier 1620 based on the waveform envelope, the efficiency of the power amplifier 1620 may be significantly improved relative to a fixed rail voltage amplifier scheme. The processor 1740 may be configured for wired or wireless communication.

In certain aspects and as discussed in further detail in connection with FIGS. 9A-9B, the programmable logic device 1660, in conjunction with the processor 1740, may implement a direct digital synthesizer (DDS) control scheme to control the waveform shape, frequency and/or amplitude of drive signals output by the generator 1100. In one aspect, for example, the programmable logic device 1660 may implement a DDS control algorithm 2680 (FIG. 9A) by recalling waveform samples stored in a dynamically-updated look-up table (LUT), such as a RAM LUT which may be embedded in an FPGA. This control algorithm is particularly useful for ultrasonic applications in which an ultrasonic transducer, such as the ultrasonic transducer 1120, may be driven by a clean sinusoidal current at its resonant frequency. Because other frequencies may excite parasitic resonances, minimizing or reducing the total distortion of the motional branch current may correspondingly minimize or reduce undesirable resonance effects. Because the waveform shape of a drive signal output by the generator 1100 is impacted by various sources of distortion present in the output drive circuit (e.g., the power transformer 1560, the power amplifier 1620), voltage and current feedback data based on the drive signal may be input into an algorithm, such as an error control algorithm implemented by the processor 1740, which compensates for distortion by suitably pre-distorting or modifying the waveform samples stored in the LUT on a dynamic, ongoing basis (e.g., in real-time). In one aspect, the amount or degree of pre-distortion applied to the LUT samples may be based on the error between a computed motional branch current and a desired current waveform shape, with the error being determined on a sample-by sample basis. In this way, the pre-distorted LUT samples, when processed through the drive circuit, may result in a motional branch drive signal having the desired waveform shape (e.g., sinusoidal) for optimally driving the ultrasonic transducer. In such aspects, the LUT waveform samples will therefore not represent the desired waveform shape of the drive signal, but rather the waveform shape that is required to ultimately produce the desired waveform shape of the motional branch drive signal when distortion effects are taken into account.

The non-isolated stage 1540 may further comprise an ADC 1780 and an ADC 1800 coupled to the output of the power transformer 1560 via respective isolation transformers 1820, 1840 for respectively sampling the voltage and current of drive signals output by the generator 1100. In certain aspects, the ADCs 1780, 1800 may be configured to sample at high speeds (e.g., 80 Msps) to enable oversampling of the drive signals. In one aspect, for example, the sampling speed of the ADCs 1780, 1800 may enable approximately 200× (depending on drive frequency) oversampling of the drive signals. In certain aspects, the sampling operations of the ADCs 1780, 1800 may be performed by a single ADC receiving input voltage and current signals via a two-way multiplexer. The use of high-speed sampling in aspects of the generator 1100 may enable, among other things, calculation of the complex current flowing through the motional branch (which may be used in certain aspects to implement DDS-based waveform shape control described above), accurate digital filtering of the sampled signals, and calculation of real power consumption with a high degree of precision. Voltage and current feedback data output by the ADCs 1780, 1800 may be received and processed (e.g., FIFO buffering, multiplexing) by the programmable logic device 1660 and stored in data memory for subsequent retrieval by, for example, the processor 1740. As noted above, voltage and current feedback data may be used as input to an algorithm for pre-distorting or modifying LUT waveform samples on a dynamic and ongoing basis. In certain aspects, this may require each stored voltage and current feedback data pair to be indexed based on, or otherwise associated with, a corresponding LUT sample that was output by the programmable logic device 1660 when the voltage and current feedback data pair was acquired. Synchronization of the LUT samples and the voltage and current feedback data in this manner contributes to the correct timing and stability of the pre-distortion algorithm.

In certain aspects, the voltage and current feedback data may be used to control the frequency and/or amplitude (e.g., current amplitude) of the drive signals. In one aspect, for example, voltage and current feedback data may be used to determine impedance phase, e.g., the phase difference between the voltage and current drive signals. The frequency of the drive signal may then be controlled to minimize or reduce the difference between the determined impedance phase and an impedance phase setpoint (e.g., 0°), thereby minimizing or reducing the effects of harmonic distortion and correspondingly enhancing impedance phase measurement accuracy. The determination of phase impedance and a frequency control signal may be implemented in the processor 1740, for example, with the frequency control signal being supplied as input to a DDS control algorithm implemented by the programmable logic device 1660.

The impedance phase may be determined through Fourier analysis. In one aspect, the phase difference between the generator voltage V_(g)(t) and generator current I_(g)(t) driving signals may be determined using the Fast Fourier Transform (FFT) or the Discrete Fourier Transform (DFT) as follows:

V_(g)(t) = A₁cos  (2π f₀t + φ₁) I_(g)(t) = A₂ cos  (2π f₀t + φ₂) ${V_{g}(f)} = {\frac{A_{1}}{2}\left( {{\delta\left( {f - f_{0}} \right)} + {\delta\left( {f + f_{0}} \right)}} \right)\exp\;\left( {j\; 2\pi\; f\frac{\varphi_{1}}{2\pi\; f_{0}}} \right)}$ ${I_{g}(f)} = {\frac{A_{2}}{2}\left( {{\delta\left( {f - f_{0}} \right)} + {\delta\left( {f + f_{0}} \right)}} \right)\;\exp\;\left( {j\; 2\pi\; f\frac{\varphi_{2}}{2\pi\; f_{0}}} \right)}$ Evaluating the Fourier Transform at the frequency of the sinusoid yields:

$\begin{matrix} {{V_{g}\left( f_{0} \right)} = {\frac{A_{1}}{2}\;{\delta(0)}\mspace{11mu}\exp\;\left( {j\;\varphi_{1}} \right)}} & {{\arg{\;\;}V\;\left( f_{0} \right)} = \varphi_{1}} \\ {{I_{g}\left( f_{0} \right)} = {\frac{A_{2}}{2}{\delta(0)}\;\exp\;\left( {j\;\varphi_{2}} \right)}} & {{\arg\mspace{11mu}{I\left( f_{0} \right)}} = \varphi_{2}} \end{matrix}$

Other approaches include weighted least-squares estimation, Kalman filtering, and space-vector-based techniques. Virtually all of the processing in an FFT or DFT technique may be performed in the digital domain with the aid of the 2-channel high speed ADC 1780, 1800, for example. In one technique, the digital signal samples of the voltage and current signals are Fourier transformed with an FFT or a DFT. The phase angle φ at any point in time can be calculated by: φ=2πft+φ ₀ where φ is the phase angle, f is the frequency, t is time, and φ₀ is the phase at t=0.

Another technique for determining the phase difference between the voltage V_(g)(t) and current I_(g)(t) signals is the zero-crossing method and produces highly accurate results. For voltage V_(g)(t) and current I_(g)(t) signals having the same frequency, each negative to positive zero-crossing of voltage signal V_(g)(t) triggers the start of a pulse, while each negative to positive zero-crossing of current signal I_(g)(t) triggers the end of the pulse. The result is a pulse train with a pulse width proportional to the phase angle between the voltage signal and the current signal. In one aspect, the pulse train may be passed through an averaging filter to yield a measure of the phase difference. Furthermore, if the positive to negative zero crossings also are used in a similar manner, and the results averaged, any effects of DC and harmonic components can be reduced. In one implementation, the analog voltage V_(g)(t) and current I_(g)(t) signals are converted to digital signals that are high if the analog signal is positive and low if the analog signal is negative. High accuracy phase estimates require sharp transitions between high and low. In one aspect, a Schmitt trigger along with an RC stabilization network may be employed to convert the analog signals into digital signals. In other aspects, an edge triggered RS flip-flop and ancillary circuitry may be employed. In yet another aspect, the zero-crossing technique may employ an eXclusive OR (XOR) gate.

Other techniques for determining the phase difference between the voltage and current signals include Lissajous figures and monitoring the image; methods such as the three-voltmeter method, the crossed-coil method, vector voltmeter and vector impedance methods; and using phase standard instruments, phase-locked loops, and other techniques as described in Phase Measurement, Peter O'Shea, 2000 CRC Press LLC, <http://www.engnetbase.com>, which is incorporated herein by reference.

In another aspect, for example, the current feedback data may be monitored in order to maintain the current amplitude of the drive signal at a current amplitude setpoint. The current amplitude setpoint may be specified directly or determined indirectly based on specified voltage amplitude and power setpoints. In certain aspects, control of the current amplitude may be implemented by control algorithm, such as, for example, a proportional-integral-derivative (PID) control algorithm, in the processor 1740. Variables controlled by the control algorithm to suitably control the current amplitude of the drive signal may include, for example, the scaling of the LUT waveform samples stored in the programmable logic device 1660 and/or the full-scale output voltage of the DAC 1680 (which supplies the input to the power amplifier 1620) via a DAC 1860.

The non-isolated stage 1540 may further comprise a processor 1900 for providing, among other things, user interface (UI) functionality. In one aspect, the processor 1900 may comprise an Atmel AT91 SAM9263 processor having an ARM 926EJ-S core, available from Atmel Corporation, San Jose, Calif., for example. Examples of UI functionality supported by the processor 1900 may include audible and visual user feedback, communication with peripheral devices (e.g., via a Universal Serial Bus (USB) interface), communication with a foot switch 1430, communication with an input device 2150 (e.g., a touch screen display) and communication with an output device 2140 (e.g., a speaker). The processor 1900 may communicate with the processor 1740 and the programmable logic device (e.g., via a serial peripheral interface (SPI) bus). Although the processor 1900 may primarily support UI functionality, it may also coordinate with the processor 1740 to implement hazard mitigation in certain aspects. For example, the processor 1900 may be programmed to monitor various aspects of user input and/or other inputs (e.g., touch screen inputs 2150, foot switch 1430 inputs, temperature sensor inputs 2160) and may disable the drive output of the generator 1100 when an erroneous condition is detected.

In certain aspects, both the processor 1740 (FIGS. 7, 8A) and the processor 1900 (FIGS. 7, 8B) may determine and monitor the operating state of the generator 1100. For processor 1740, the operating state of the generator 1100 may dictate, for example, which control and/or diagnostic processes are implemented by the processor 1740. For processor 1900, the operating state of the generator 1100 may dictate, for example, which elements of a user interface (e.g., display screens, sounds) are presented to a user. The processors 1740, 1900 may independently maintain the current operating state of the generator 1100 and recognize and evaluate possible transitions out of the current operating state. The processor 1740 may function as the master in this relationship and determine when transitions between operating states are to occur. The processor 1900 may be aware of valid transitions between operating states and may confirm if a particular transition is appropriate. For example, when the processor 1740 instructs the processor 1900 to transition to a specific state, the processor 1900 may verify that the requested transition is valid. In the event that a requested transition between states is determined to be invalid by the processor 1900, the processor 1900 may cause the generator 1100 to enter a failure mode.

The non-isolated stage 1540 may further comprise a controller 1960 (FIGS. 7, 8B) for monitoring input devices 2150 (e.g., a capacitive touch sensor used for turning the generator 1100 on and off, a capacitive touch screen). In certain aspects, the controller 1960 may comprise at least one processor and/or other controller device in communication with the processor 1900. In one aspect, for example, the controller 1960 may comprise a processor (e.g., a Mega168 8-bit controller available from Atmel) configured to monitor user input provided via one or more capacitive touch sensors. In one aspect, the controller 1960 may comprise a touch screen controller (e.g., a QT5480 touch screen controller available from Atmel) to control and manage the acquisition of touch data from a capacitive touch screen.

In certain aspects, when the generator 1100 is in a “power off” state, the controller 1960 may continue to receive operating power (e.g., via a line from a power supply of the generator 1100, such as the power supply 2110 (FIG. 7) discussed below). In this way, the controller 1960 may continue to monitor an input device 2150 (e.g., a capacitive touch sensor located on a front panel of the generator 1100) for turning the generator 1100 on and off. When the generator 1100 is in the “power off” state, the controller 1960 may wake the power supply (e.g., enable operation of one or more DC/DC voltage converters 2130 (FIG. 7) of the power supply 2110) if activation of the “on/off” input device 2150 by a user is detected. The controller 1960 may therefore initiate a sequence for transitioning the generator 1100 to a “power on” state. Conversely, the controller 1960 may initiate a sequence for transitioning the generator 1100 to the “power off” state if activation of the “on/off” input device 2150 is detected when the generator 1100 is in the “power on” state. In certain aspects, for example, the controller 1960 may report activation of the “on/off” input device 2150 to the processor 1900, which in turn implements the necessary process sequence for transitioning the generator 1100 to the “power off” state. In such aspects, the controller 1960 may have no independent ability for causing the removal of power from the generator 1100 after its “power on” state has been established.

In certain aspects, the controller 1960 may cause the generator 1100 to provide audible or other sensory feedback for alerting the user that a “power on” or “power off” sequence has been initiated. Such an alert may be provided at the beginning of a “power on” or “power off” sequence and prior to the commencement of other processes associated with the sequence.

In certain aspects, the isolated stage 1520 may comprise an instrument interface circuit 1980 to, for example, provide a communication interface between a control circuit of a surgical device (e.g., a control circuit comprising handpiece switches) and components of the non-isolated stage 1540, such as, for example, the programmable logic device 1660, the processor 1740 and/or the processor 1900. The instrument interface circuit 1980 may exchange information with components of the non-isolated stage 1540 via a communication link that maintains a suitable degree of electrical isolation between the stages 1520, 1540, such as, for example, an infrared (IR)-based communication link. Power may be supplied to the instrument interface circuit 1980 using, for example, a low-dropout voltage regulator powered by an isolation transformer driven from the non-isolated stage 1540.

In one aspect, the instrument interface circuit 1980 may comprise a programmable logic device 2000 (e.g., an FPGA) in communication with a signal conditioning circuit 2020 (FIG. 7 and FIG. 8C). The signal conditioning circuit 2020 may be configured to receive a periodic signal from the programmable logic device 2000 (e.g., a 2 kHz square wave) to generate a bipolar interrogation signal having an identical frequency. The interrogation signal may be generated, for example, using a bipolar current source fed by a differential amplifier. The interrogation signal may be communicated to a surgical device control circuit (e.g., by using a conductive pair in a cable that connects the generator 1100 to the surgical device) and monitored to determine a state or configuration of the control circuit. For example, the control circuit may comprise a number of switches, resistors and/or diodes to modify one or more characteristics (e.g., amplitude, rectification) of the interrogation signal such that a state or configuration of the control circuit is uniquely discernible based on the one or more characteristics. In one aspect, for example, the signal conditioning circuit 2020 may comprise an ADC for generating samples of a voltage signal appearing across inputs of the control circuit resulting from passage of interrogation signal therethrough. The programmable logic device 2000 (or a component of the non-isolated stage 1540) may then determine the state or configuration of the control circuit based on the ADC samples.

In one aspect, the instrument interface circuit 1980 may comprise a first data circuit interface 2040 to enable information exchange between the programmable logic device 2000 (or other element of the instrument interface circuit 1980) and a first data circuit disposed in or otherwise associated with a surgical device. In certain aspects, for example, a first data circuit 2060 may be disposed in a cable integrally attached to a surgical device handpiece, or in an adaptor for interfacing a specific surgical device type or model with the generator 1100. In certain aspects, the first data circuit may comprise a non-volatile storage device, such as an electrically erasable programmable read-only memory (EEPROM) device. In certain aspects and referring again to FIG. 7, the first data circuit interface 2040 may be implemented separately from the programmable logic device 2000 and comprise suitable circuitry (e.g., discrete logic devices, a processor) to enable communication between the programmable logic device 2000 and the first data circuit. In other aspects, the first data circuit interface 2040 may be integral with the programmable logic device 2000.

In certain aspects, the first data circuit 2060 may store information pertaining to the particular surgical device with which it is associated. Such information may include, for example, a model number, a serial number, a number of operations in which the surgical device has been used, and/or any other type of information. This information may be read by the instrument interface circuit 1980 (e.g., by the programmable logic device 2000), transferred to a component of the non-isolated stage 1540 (e.g., to programmable logic device 1660, processor 1740 and/or processor 1900) for presentation to a user via an output device 2140 and/or for controlling a function or operation of the generator 1100. Additionally, any type of information may be communicated to first data circuit 2060 for storage therein via the first data circuit interface 2040 (e.g., using the programmable logic device 2000). Such information may comprise, for example, an updated number of operations in which the surgical device has been used and/or dates and/or times of its usage.

As discussed previously, a surgical instrument may be detachable from a handpiece (e.g., instrument 1106 may be detachable from handpiece 1107) to promote instrument interchangeability and/or disposability. In such cases, known generators may be limited in their ability to recognize particular instrument configurations being used and to optimize control and diagnostic processes accordingly. The addition of readable data circuits to surgical device instruments to address this issue is problematic from a compatibility standpoint, however. For example, it may be impractical to design a surgical device to maintain backward compatibility with generators that lack the requisite data reading functionality due to, for example, differing signal schemes, design complexity and cost. Other aspects of instruments address these concerns by using data circuits that may be implemented in existing surgical instruments economically and with minimal design changes to preserve compatibility of the surgical devices with current generator platforms.

Additionally, aspects of the generator 1100 may enable communication with instrument-based data circuits. For example, the generator 1100 may be configured to communicate with a second data circuit (e.g., a data circuit) contained in an instrument (e.g., instrument 1104, 1106 or 1108) of a surgical device. The instrument interface circuit 1980 may comprise a second data circuit interface 2100 to enable this communication. In one aspect, the second data circuit interface 2100 may comprise a tri-state digital interface, although other interfaces may also be used. In certain aspects, the second data circuit may generally be any circuit for transmitting and/or receiving data. In one aspect, for example, the second data circuit may store information pertaining to the particular surgical instrument with which it is associated. Such information may include, for example, a model number, a serial number, a number of operations in which the surgical instrument has been used, and/or any other type of information. Additionally or alternatively, any type of information may be communicated to the second data circuit for storage therein via the second data circuit interface 2100 (e.g., using the programmable logic device 2000). Such information may comprise, for example, an updated number of operations in which the instrument has been used and/or dates and/or times of its usage. In certain aspects, the second data circuit may transmit data acquired by one or more sensors (e.g., an instrument-based temperature sensor). In certain aspects, the second data circuit may receive data from the generator 1100 and provide an indication to a user (e.g., an LED indication or other visible indication) based on the received data.

In certain aspects, the second data circuit and the second data circuit interface 2100 may be configured such that communication between the programmable logic device 2000 and the second data circuit can be effected without the need to provide additional conductors for this purpose (e.g., dedicated conductors of a cable connecting a handpiece to the generator 1100). In one aspect, for example, information may be communicated to and from the second data circuit using a one-wire bus communication scheme implemented on existing cabling, such as one of the conductors used transmit interrogation signals from the signal conditioning circuit 2020 to a control circuit in a handpiece. In this way, design changes or modifications to the surgical device that might otherwise be necessary are minimized or reduced. Moreover, because different types of communications can be implemented over a common physical channel (either with or without frequency-band separation), the presence of a second data circuit may be “invisible” to generators that do not have the requisite data reading functionality, thus enabling backward compatibility of the surgical device instrument.

In certain aspects, the isolated stage 1520 may comprise at least one blocking capacitor 2960-1 (FIG. 8C) connected to the drive signal output 1600 b to prevent passage of DC current to a patient. A single blocking capacitor may be required to comply with medical regulations or standards, for example. While failure in single-capacitor designs is relatively uncommon, such failure may nonetheless have negative consequences. In one aspect, a second blocking capacitor 2960-2 may be provided in series with the blocking capacitor 2960-1, with current leakage from a point between the blocking capacitors 2960-1, 2960-2 being monitored by, for example, an ADC 2980 for sampling a voltage induced by leakage current. The samples may be received by the programmable logic device 2000, for example. Based on changes in the leakage current (as indicated by the voltage samples in the aspect of FIG. 7), the generator 1100 may determine when at least one of the blocking capacitors 2960-1, 2960-2 has failed. Accordingly, the aspect of FIG. 7 may provide a benefit over single-capacitor designs having a single point of failure.

In certain aspects, the non-isolated stage 1540 may comprise a power supply 2110 for outputting DC power at a suitable voltage and current. The power supply may comprise, for example, a 400 W power supply for outputting a 48 VDC system voltage. As discussed above, the power supply 2110 may further comprise one or more DC/DC voltage converters 2130 for receiving the output of the power supply to generate DC outputs at the voltages and currents required by the various components of the generator 1100. As discussed above in connection with the controller 1960, one or more of the DC/DC voltage converters 2130 may receive an input from the controller 1960 when activation of the “on/off” input device 2150 by a user is detected by the controller 1960 to enable operation of, or wake, the DC/DC voltage converters 2130.

FIGS. 9A-9B illustrate certain functional and structural aspects of one aspect of the generator 1100. Feedback indicating current and voltage output from the secondary winding 1580 of the power transformer 1560 is received by the ADCs 1780, 1800, respectively. As shown, the ADCs 1780, 1800 may be implemented as a 2-channel ADC and may sample the feedback signals at a high speed (e.g., 80 Msps) to enable oversampling (e.g., approximately 200× oversampling) of the drive signals. The current and voltage feedback signals may be suitably conditioned in the analog domain (e.g., amplified, filtered) prior to processing by the ADCs 1780, 1800. Current and voltage feedback samples from the ADCs 1780, 1800 may be individually buffered and subsequently multiplexed or interleaved into a single data stream within block 2120 of the programmable logic device 1660. In the aspect of FIGS. 9A-9B, the programmable logic device 1660 comprises an FPGA.

The multiplexed current and voltage feedback samples may be received by a parallel data acquisition port (PDAP) implemented within block 2144 of the processor 1740. The PDAP may comprise a packing unit for implementing any of a number of methodologies for correlating the multiplexed feedback samples with a memory address. In one aspect, for example, feedback samples corresponding to a particular LUT sample output by the programmable logic device 1660 may be stored at one or more memory addresses that are correlated or indexed with the LUT address of the LUT sample. In another aspect, feedback samples corresponding to a particular LUT sample output by the programmable logic device 1660 may be stored, along with the LUT address of the LUT sample, at a common memory location. In any event, the feedback samples may be stored such that the address of the LUT sample from which a particular set of feedback samples originated may be subsequently ascertained. As discussed above, synchronization of the LUT sample addresses and the feedback samples in this way contributes to the correct timing and stability of the pre-distortion algorithm. A direct memory access (DMA) controller implemented at block 2166 of the processor 1740 may store the feedback samples (and any LUT sample address data, where applicable) at a designated memory location 2180 of the processor 1740 (e.g., internal RAM).

Block 2200 of the processor 1740 may implement a pre-distortion algorithm for pre-distorting or modifying the LUT samples stored in the programmable logic device 1660 on a dynamic, ongoing basis. As discussed above, pre-distortion of the LUT samples may compensate for various sources of distortion present in the output drive circuit of the generator 1100. The pre-distorted LUT samples, when processed through the drive circuit, will therefore result in a drive signal having the desired waveform shape (e.g., sinusoidal) for optimally driving the ultrasonic transducer.

At block 2220 of the pre-distortion algorithm, the current through the motional branch of the ultrasonic transducer is determined. The motional branch current may be determined using Kirchhoff's Current Law based on, for example, the current and voltage feedback samples stored at memory location 2180 (which, when suitably scaled, may be representative of I_(g) and V_(g) in the model of FIG. 6 discussed above), a value of the ultrasonic transducer static capacitance C₀ (measured or known a priori) and a known value of the drive frequency. A motional branch current sample for each set of stored current and voltage feedback samples associated with a LUT sample may be determined.

At block 2240 of the pre-distortion algorithm, each motional branch current sample determined at block 2220 is compared to a sample of a desired current waveform shape to determine a difference, or sample amplitude error, between the compared samples. For this determination, the sample of the desired current waveform shape may be supplied, for example, from a waveform shape LUT 2260 containing amplitude samples for one cycle of a desired current waveform shape. The particular sample of the desired current waveform shape from the LUT 2260 used for the comparison may be dictated by the LUT sample address associated with the motional branch current sample used in the comparison. Accordingly, the input of the motional branch current to block 2240 may be synchronized with the input of its associated LUT sample address to block 2240. The LUT samples stored in the programmable logic device 1660 and the LUT samples stored in the waveform shape LUT 2260 may therefore be equal in number. In certain aspects, the desired current waveform shape represented by the LUT samples stored in the waveform shape LUT 2260 may be a fundamental sine wave. Other waveform shapes may be desirable. For example, it is contemplated that a fundamental sine wave for driving main longitudinal motion of an ultrasonic transducer superimposed with one or more other drive signals at other frequencies, such as a third order harmonic for driving at least two mechanical resonances for beneficial vibrations of transverse or other modes, could be used.

Each value of the sample amplitude error determined at block 2240 may be transmitted to the LUT of the programmable logic device 1660 (shown at block 2280 in FIG. 9A) along with an indication of its associated LUT address. Based on the value of the sample amplitude error and its associated address (and, optionally, values of sample amplitude error for the same LUT address previously received), the LUT 2280 (or other control block of the programmable logic device 1660) may pre-distort or modify the value of the LUT sample stored at the LUT address such that the sample amplitude error is reduced or minimized. It will be appreciated that such pre-distortion or modification of each LUT sample in an iterative manner across the entire range of LUT addresses will cause the waveform shape of the generator's output current to match or conform to the desired current waveform shape represented by the samples of the waveform shape LUT 2260.

Current and voltage amplitude measurements, power measurements and impedance measurements may be determined at block 2300 of the processor 1740 based on the current and voltage feedback samples stored at memory location 2180. Prior to the determination of these quantities, the feedback samples may be suitably scaled and, in certain aspects, processed through a suitable filter 2320 to remove noise resulting from, for example, the data acquisition process and induced harmonic components. The filtered voltage and current samples may therefore substantially represent the fundamental frequency of the generator's drive output signal. In certain aspects, the filter 2320 may be a finite impulse response (FIR) filter applied in the frequency domain. Such aspects may use the Fast Fourier Transform (FFT) of the output drive signal current and voltage signals. In certain aspects, the resulting frequency spectrum may be used to provide additional generator functionality. In one aspect, for example, the ratio of the second and/or third order harmonic component relative to the fundamental frequency component may be used as a diagnostic indicator.

At block 2340 (FIG. 9B), a root mean square (RMS) calculation may be applied to a sample size of the current feedback samples representing an integral number of cycles of the drive signal to generate a measurement I_(rms) representing the drive signal output current.

At block 2360, a root mean square (RMS) calculation may be applied to a sample size of the voltage feedback samples representing an integral number of cycles of the drive signal to determine a measurement V_(rms) representing the drive signal output voltage.

At block 2380, the current and voltage feedback samples may be multiplied point by point, and a mean calculation is applied to samples representing an integral number of cycles of the drive signal to determine a measurement P, of the generator's real output power.

At block 2400, measurement P_(a) of the generator's apparent output power may be determined as the product V_(rms)·I_(rms).

At block 2420, measurement Z_(m) of the load impedance magnitude may be determined as the quotient V_(rms)/I_(rms).

In certain aspects, the quantities I_(rms), V_(rms), P_(r), P_(a) and Z_(m) determined at blocks 2340, 2360, 2380, 2400 and 2420 may be used by the generator 1100 to implement any of a number of control and/or diagnostic processes. In certain aspects, any of these quantities may be communicated to a user via, for example, an output device 2140 integral with the generator 1100 or an output device 2140 connected to the generator 1100 through a suitable communication interface (e.g., a USB interface). Various diagnostic processes may include, without limitation, handpiece integrity, instrument integrity, instrument attachment integrity, instrument overload, approaching instrument overload, frequency lock failure, over-voltage condition, over-current condition, over-power condition, voltage sense failure, current sense failure, audio indication failure, visual indication failure, short circuit condition, power delivery failure, or blocking capacitor failure, for example.

Block 2440 of the processor 1740 may implement a phase control algorithm for determining and controlling the impedance phase of an electrical load (e.g., the ultrasonic transducer) driven by the generator 1100. As discussed above, by controlling the frequency of the drive signal to minimize or reduce the difference between the determined impedance phase and an impedance phase setpoint (e.g., 0°), the effects of harmonic distortion may be minimized or reduced, and the accuracy of the phase measurement increased.

The phase control algorithm receives as input the current and voltage feedback samples stored in the memory location 2180. Prior to their use in the phase control algorithm, the feedback samples may be suitably scaled and, in certain aspects, processed through a suitable filter 2460 (which may be identical to filter 2320) to remove noise resulting from the data acquisition process and induced harmonic components, for example. The filtered voltage and current samples may therefore substantially represent the fundamental frequency of the generator's drive output signal.

At block 2480 of the phase control algorithm, the current through the motional branch of the ultrasonic transducer is determined. This determination may be identical to that described above in connection with block 2220 of the pre-distortion algorithm. The output of block 2480 may thus be, for each set of stored current and voltage feedback samples associated with a LUT sample, a motional branch current sample.

At block 2500 of the phase control algorithm, impedance phase is determined based on the synchronized input of motional branch current samples determined at block 2480 and corresponding voltage feedback samples. In certain aspects, the impedance phase is determined as the average of the impedance phase measured at the rising edge of the waveforms and the impedance phase measured at the falling edge of the waveforms.

At block 2520 of the of the phase control algorithm, the value of the impedance phase determined at block 2220 is compared to phase setpoint 2540 to determine a difference, or phase error, between the compared values.

At block 2560 (FIG. 9A) of the phase control algorithm, based on a value of phase error determined at block 2520 and the impedance magnitude determined at block 2420, a frequency output for controlling the frequency of the drive signal is determined. The value of the frequency output may be continuously adjusted by the block 2560 and transferred to a DDS control block 2680 (discussed below) in order to maintain the impedance phase determined at block 2500 at the phase setpoint (e.g., zero phase error). In certain aspects, the impedance phase may be regulated to a 0° phase setpoint. In this way, any harmonic distortion will be centered about the crest of the voltage waveform, enhancing the accuracy of phase impedance determination.

Block 2580 of the processor 1740 may implement an algorithm for modulating the current amplitude of the drive signal in order to control the drive signal current, voltage and power in accordance with user specified setpoints, or in accordance with requirements specified by other processes or algorithms implemented by the generator 1100. Control of these quantities may be realized, for example, by scaling the LUT samples in the LUT 2280 and/or by adjusting the full-scale output voltage of the DAC 1680 (which supplies the input to the power amplifier 1620) via a DAC 1860. Block 2600 (which may be implemented as a PID controller in certain aspects) may receive, as input, current feedback samples (which may be suitably scaled and filtered) from the memory location 2180. The current feedback samples may be compared to a “current demand” I_(d) value dictated by the controlled variable (e.g., current, voltage or power) to determine if the drive signal is supplying the necessary current. In aspects in which drive signal current is the control variable, the current demand I_(d) may be specified directly by a current setpoint 2620A (I_(sp)). For example, an RMS value of the current feedback data (determined as in block 2340) may be compared to user-specified RMS current setpoint I_(sp) to determine the appropriate controller action. If, for example, the current feedback data indicates an RMS value less than the current setpoint I_(sp), LUT scaling and/or the full-scale output voltage of the DAC 1680 may be adjusted by the block 2600 such that the drive signal current is increased. Conversely, block 2600 may adjust LUT scaling and/or the full-scale output voltage of the DAC 1680 to decrease the drive signal current when the current feedback data indicates an RMS value greater than the current setpoint I_(sp).

In aspects in which the drive signal voltage is the control variable, the current demand I_(d) may be specified indirectly, for example, based on the current required to maintain a desired voltage setpoint 2620B (V_(sp)) given the load impedance magnitude Z_(m) measured at block 2420 (e.g. I_(d)=V_(sp)/Z_(m)). Similarly, in aspects in which drive signal power is the control variable, the current demand I_(d) may be specified indirectly, for example, based on the current required to maintain a desired power setpoint 2620C (P_(sp)) given the voltage V_(rms) measured at blocks 2360 (e.g. I_(d)=P_(sp)/V_(rms)).

Block 2680 (FIG. 9A) may implement a DDS control algorithm for controlling the drive signal by recalling LUT samples stored in the LUT 2280. In certain aspects, the DDS control algorithm may be a numerically-controlled oscillator (NCO) algorithm for generating samples of a waveform at a fixed clock rate using a point (memory location)-skipping technique. The NCO algorithm may implement a phase accumulator, or frequency-to-phase converter, that functions as an address pointer for recalling LUT samples from the LUT 2280. In one aspect, the phase accumulator may be a D step size, modulo N phase accumulator, where D is a positive integer representing a frequency control value, and N is the number of LUT samples in the LUT 2280. A frequency control value of D=1, for example, may cause the phase accumulator to sequentially point to every address of the LUT 2280, resulting in a waveform output replicating the waveform stored in the LUT 2280. When D>1, the phase accumulator may skip addresses in the LUT 2280, resulting in a waveform output having a higher frequency. Accordingly, the frequency of the waveform generated by the DDS control algorithm may therefore be controlled by suitably varying the frequency control value. In certain aspects, the frequency control value may be determined based on the output of the phase control algorithm implemented at block 2440. The output of block 2680 may supply the input of DAC 1680, which in turn supplies a corresponding analog signal to an input of the power amplifier 1620.

Block 2700 of the processor 1740 may implement a switch-mode converter control algorithm for dynamically modulating the rail voltage of the power amplifier 1620 based on the waveform envelope of the signal being amplified, thereby improving the efficiency of the power amplifier 1620. In certain aspects, characteristics of the waveform envelope may be determined by monitoring one or more signals contained in the power amplifier 1620. In one aspect, for example, characteristics of the waveform envelope may be determined by monitoring the minima of a drain voltage (e.g., a MOSFET drain voltage) that is modulated in accordance with the envelope of the amplified signal. A minima voltage signal may be generated, for example, by a voltage minima detector coupled to the drain voltage. The minima voltage signal may be sampled by ADC 1760, with the output minima voltage samples being received at block 2720 of the switch-mode converter control algorithm. Based on the values of the minima voltage samples, block 2740 may control a PWM signal output by a PWM generator 2760, which, in turn, controls the rail voltage supplied to the power amplifier 1620 by the switch-mode regulator 1700. In certain aspects, as long as the values of the minima voltage samples are less than a minima target 2780 input into block 2720, the rail voltage may be modulated in accordance with the waveform envelope as characterized by the minima voltage samples. When the minima voltage samples indicate low envelope power levels, for example, block 2740 may cause a low rail voltage to be supplied to the power amplifier 1620, with the full rail voltage being supplied only when the minima voltage samples indicate maximum envelope power levels. When the minima voltage samples fall below the minima target 2780, block 2740 may cause the rail voltage to be maintained at a minimum value suitable for ensuring proper operation of the power amplifier 1620.

FIG. 10 illustrates a control circuit 500 configured to control aspects of the surgical instrument or tool according to one aspect of this disclosure. The control circuit 500 can be configured to implement various processes described herein. The control circuit 500 may comprise a microcontroller comprising one or more processors 502 (e.g., microprocessor, microcontroller) coupled to at least one memory circuit 504. The memory circuit 504 stores machine-executable instructions that, when executed by the processor 502, cause the processor 502 to execute machine instructions to implement various processes described herein. The processor 502 may be any one of a number of single-core or multicore processors known in the art. The memory circuit 504 may comprise volatile and non-volatile storage media. The processor 502 may include an instruction processing unit 506 and an arithmetic unit 508. The instruction processing unit may be configured to receive instructions from the memory circuit 504 of this disclosure.

FIG. 11 illustrates a combinational logic circuit 510 configured to control aspects of the surgical instrument or tool according to one aspect of this disclosure. The combinational logic circuit 510 can be configured to implement various processes described herein. The combinational logic circuit 510 may comprise a finite state machine comprising a combinational logic 512 configured to receive data associated with the surgical instrument or tool at an input 514, process the data by the combinational logic 512, and provide an output 516.

FIG. 12 illustrates a sequential logic circuit 520 configured to control aspects of the surgical instrument or tool according to one aspect of this disclosure. The sequential logic circuit 520 or the combinational logic 522 can be configured to implement various processes described herein. The sequential logic circuit 520 may comprise a finite state machine. The sequential logic circuit 520 may comprise a combinational logic 522, at least one memory circuit 524, and a clock 529, for example. The at least one memory circuit 524 can store a current state of the finite state machine. In certain instances, the sequential logic circuit 520 may be synchronous or asynchronous. The combinational logic 522 is configured to receive data associated with the surgical instrument or tool from an input 526, process the data by the combinational logic 522, and provide an output 528. In other aspects, the circuit may comprise a combination of a processor (e.g., processor 502, FIG. 13) and a finite state machine to implement various processes herein. In other aspects, the finite state machine may comprise a combination of a combinational logic circuit (e.g., combinational logic circuit 510, FIG. 14) and the sequential logic circuit 520.

In one aspect, the ultrasonic or high-frequency current generators of the surgical system 1000 may be configured to generate the electrical signal waveform digitally such that the desired using a predetermined number of phase points stored in a lookup table to digitize the wave shape. The phase points may be stored in a table defined in a memory, a field programmable gate array (FPGA), or any suitable non-volatile memory. FIG. 13 illustrates one aspect of a fundamental architecture for a digital synthesis circuit such as a direct digital synthesis (DDS) circuit 4100 configured to generate a plurality of wave shapes for the electrical signal waveform. The generator software and digital controls may command the FPGA to scan the addresses in the lookup table 4104 which in turn provides varying digital input values to a DAC circuit 4108 that feeds a power amplifier. The addresses may be scanned according to a frequency of interest. Using such a lookup table 4104 enables generating various types of wave shapes that can be fed into tissue or into a transducer, an RF electrode, multiple transducers simultaneously, multiple RF electrodes simultaneously, or a combination of RF and ultrasonic instruments. Furthermore, multiple lookup tables 4104 that represent multiple wave shapes can be created, stored, and applied to tissue from a generator.

The waveform signal may be configured to control at least one of an output current, an output voltage, or an output power of an ultrasonic transducer and/or an RF electrode, or multiples thereof (e.g. two or more ultrasonic transducers and/or two or more RF electrodes). Further, where the surgical instrument comprises an ultrasonic components, the waveform signal may be configured to drive at least two vibration modes of an ultrasonic transducer of the at least one surgical instrument. Accordingly, a generator may be configured to provide a waveform signal to at least one surgical instrument wherein the waveform signal corresponds to at least one wave shape of a plurality of wave shapes in a table. Further, the waveform signal provided to the two surgical instruments may comprise two or more wave shapes. The table may comprise information associated with a plurality of wave shapes and the table may be stored within the generator. In one aspect or example, the table may be a direct digital synthesis table, which may be stored in an FPGA of the generator. The table may be addressed by anyway that is convenient for categorizing wave shapes. According to one aspect, the table, which may be a direct digital synthesis table, is addressed according to a frequency of the waveform signal. Additionally, the information associated with the plurality of wave shapes may be stored as digital information in the table.

The analog electrical signal waveform may be configured to control at least one of an output current, an output voltage, or an output power of an ultrasonic transducer and/or an RF electrode, or multiples thereof (e.g., two or more ultrasonic transducers and/or two or more RF electrodes). Further, where the surgical instrument comprises ultrasonic components, the analog electrical signal waveform may be configured to drive at least two vibration modes of an ultrasonic transducer of the at least one surgical instrument. Accordingly, the generator circuit may be configured to provide an analog electrical signal waveform to at least one surgical instrument wherein the analog electrical signal waveform corresponds to at least one wave shape of a plurality of wave shapes stored in a lookup table 4104. Further, the analog electrical signal waveform provided to the two surgical instruments may comprise two or more wave shapes. The lookup table 4104 may comprise information associated with a plurality of wave shapes and the lookup table 4104 may be stored either within the generator circuit or the surgical instrument. In one aspect or example, the lookup table 4104 may be a direct digital synthesis table, which may be stored in an FPGA of the generator circuit or the surgical instrument. The lookup table 4104 may be addressed by anyway that is convenient for categorizing wave shapes. According to one aspect, the lookup table 4104, which may be a direct digital synthesis table, is addressed according to a frequency of the desired analog electrical signal waveform. Additionally, the information associated with the plurality of wave shapes may be stored as digital information in the lookup table 4104.

With the widespread use of digital techniques in instrumentation and communications systems, a digitally-controlled method of generating multiple frequencies from a reference frequency source has evolved and is referred to as direct digital synthesis. The basic architecture is shown in FIG. 13. In this simplified block diagram, a DDS circuit is coupled to a processor, controller, or a logic device of the generator circuit and to a memory circuit located in the generator circuit of the surgical system 1000. The DDS circuit 4100 comprises an address counter 4102, lookup table 4104, a register 4106, a DAC circuit 4108, and a filter 4112. A stable clock f_(c) is received by the address counter 4102 and the register 4106 drives a programmable-read-only-memory (PROM) which stores one or more integral number of cycles of a sinewave (or other arbitrary waveform) in a lookup table 4104. As the address counter 4102 steps through memory locations, values stored in the lookup table 4104 are written to the register 4106, which is coupled to the DAC circuit 4108. The corresponding digital amplitude of the signal at the memory location of the lookup table 4104 drives the DAC circuit 4108, which in turn generates an analog output signal 4110. The spectral purity of the analog output signal 4110 is determined primarily by the DAC circuit 4108. The phase noise is basically that of the reference clock f_(c). The first analog output signal 4110 output from the DAC circuit 4108 is filtered by the filter 4112 and a second analog output signal 4114 output by the filter 4112 is provided to an amplifier having an output coupled to the output of the generator circuit. The second analog output signal has a frequency f_(out).

Because the DDS circuit 4100 is a sampled data system, issues involved in sampling must be considered: quantization noise, aliasing, filtering, etc. For instance, the higher order harmonics of the DAC circuit 4108 output frequencies fold back into the Nyquist bandwidth, making them unfilterable, whereas, the higher order harmonics of the output of phase-locked-loop (PLL) based synthesizers can be filtered. The lookup table 4104 contains signal data for an integral number of cycles. The final output frequency f_(out) can be changed changing the reference clock frequency f_(c) or by reprogramming the PROM.

The DDS circuit 4100 may comprise multiple lookup tables 4104 where the lookup table 4104 stores a waveform represented by a predetermined number of samples, wherein the samples define a predetermined shape of the waveform. Thus multiple waveforms having a unique shape can be stored in multiple lookup tables 4104 to provide different tissue treatments based on instrument settings or tissue feedback. Examples of waveforms include high crest factor RF electrical signal waveforms for surface tissue coagulation, low crest factor RF electrical signal waveform for deeper tissue penetration, and electrical signal waveforms that promote efficient touch-up coagulation. In one aspect, the DDS circuit 4100 can create multiple wave shape lookup tables 4104 and during a tissue treatment procedure (e.g., “on-the-fly” or in virtual real time based on user or sensor inputs) switch between different wave shapes stored in separate lookup tables 4104 based on the tissue effect desired and/or tissue feedback. Accordingly, switching between wave shapes can be based on tissue impedance and other factors, for example. In other aspects, the lookup tables 4104 can store electrical signal waveforms shaped to maximize the power delivered into the tissue per cycle (i.e., trapezoidal or square wave). In other aspects, the lookup tables 4104 can store wave shapes synchronized in such way that they make maximizing power delivery by the multifunction surgical instrument of surgical system 1000 while delivering RF and ultrasonic drive signals. In yet other aspects, the lookup tables 4104 can store electrical signal waveforms to drive ultrasonic and RF therapeutic, and/or sub-therapeutic, energy simultaneously while maintaining ultrasonic frequency lock. Custom wave shapes specific to different instruments and their tissue effects can be stored in the non-volatile memory of the generator circuit or in the non-volatile memory (e.g., EEPROM) of the surgical system 1000 and be fetched upon connecting the multifunction surgical instrument to the generator circuit. An example of an exponentially damped sinusoid, as used in many high crest factor “coagulation” waveforms is shown in FIG. 15.

A more flexible and efficient implementation of the DDS circuit 4100 employs a digital circuit called a Numerically Controlled Oscillator (NCO). A block diagram of a more flexible and efficient digital synthesis circuit such as a DDS circuit 4200 is shown in FIG. 14. In this simplified block diagram, a DDS circuit 4200 is coupled to a processor, controller, or a logic device of the generator and to a memory circuit located either in the generator or in any of the surgical instruments of surgical system 1000. The DDS circuit 4200 comprises a load register 4202, a parallel delta phase register 4204, an adder circuit 4216, a phase register 4208, a lookup table 4210 (phase-to-amplitude converter), a DAC circuit 4212, and a filter 4214. The adder circuit 4216 and the phase register 4208 form part of a phase accumulator 4206. A clock frequency f_(c) is applied to the phase register 4208 and a DAC circuit 4212. The load register 4202 receives a tuning word that specifies output frequency as a fraction of the reference clock frequency signal f_(c). The output of the load register 4202 is provided to the parallel delta phase register 4204 with a tuning word M.

The DDS circuit 4200 includes a sample clock that generates the clock frequency f_(c), the phase accumulator 4206, and the lookup table 4210 (e.g., phase to amplitude converter). The content of the phase accumulator 4206 is updated once per clock cycle f_(c). When time the phase accumulator 4206 is updated, the digital number, M, stored in the parallel delta phase register 4204 is added to the number in the phase register 4208 by the adder circuit 4216. Assuming that the number in the parallel delta phase register 4204 is 00 . . . 01 and that the initial contents of the phase accumulator 4206 is 00 . . . 00. The phase accumulator 4206 is updated by 00 . . . 01 per clock cycle. If the phase accumulator 4206 is 32-bits wide, 232 clock cycles (over 4 billion) are required before the phase accumulator 4206 returns to 00 . . . 00, and the cycle repeats.

A truncated output 4218 of the phase accumulator 4206 is provided to a phase-to amplitude converter lookup table 4210 and the output of the lookup table 4210 is coupled to a DAC circuit 4212. The truncated output 4218 of the phase accumulator 4206 serves as the address to a sine (or cosine) lookup table. An address in the lookup table corresponds to a phase point on the sinewave from 0° to 360°. The lookup table 4210 contains the corresponding digital amplitude information for one complete cycle of a sinewave. The lookup table 4210 therefore maps the phase information from the phase accumulator 4206 into a digital amplitude word, which in turn drives the DAC circuit 4212. The output of the DAC circuit is a first analog signal 4220 and is filtered by a filter 4214. The output of the filter 4214 is a second analog signal 4222, which is provided to a power amplifier coupled to the output of the generator circuit.

In one aspect, the electrical signal waveform may be digitized into 1024 (210) phase points, although the wave shape may be digitized is any suitable number of 2n phase points ranging from 256 (28) to 281,474,976,710,656 (248), where n is a positive integer, as shown in TABLE 1. The electrical signal waveform may be expressed as A_(n)(θ_(n)), where a normalized amplitude A_(n) at a point n is represented by a phase angle θ_(n) is referred to as a phase point at point n. The number of discrete phase points n determines the tuning resolution of the DDS circuit 4200 (as well as the DDS circuit 4100 shown in FIG. 13).

TABLE 1 specifies the electrical signal waveform digitized into a number of phase points.

TABLE 1 N Number of Phase Points 2^(n)  8 256 10 1,024 12 4,096 14 16,384 16 65,536 18 262,144 20 1,048,576 22 4,194,304 24 16,777,216 26 67,108,864 28 268,435,456 . . . . . . 32 4,294,967,296 . . . . . . 48 281,474,976,710,656 . . . . . .

The generator circuit algorithms and digital control circuits scan the addresses in the lookup table 4210, which in turn provides varying digital input values to the DAC circuit 4212 that feeds the filter 4214 and the power amplifier. The addresses may be scanned according to a frequency of interest. Using the lookup table enables generating various types of shapes that can be converted into an analog output signal by the DAC circuit 4212, filtered by the filter 4214, amplified by the power amplifier coupled to the output of the generator circuit, and fed to the tissue in the form of RF energy or fed to an ultrasonic transducer and applied to the tissue in the form of ultrasonic vibrations which deliver energy to the tissue in the form of heat. The output of the amplifier can be applied to an RF electrode, multiple RF electrodes simultaneously, an ultrasonic transducer, multiple ultrasonic transducers simultaneously, or a combination of RF and ultrasonic transducers, for example. Furthermore, multiple wave shape tables can be created, stored, and applied to tissue from a generator circuit.

With reference back to FIG. 13, for n=32, and M=1, the phase accumulator 4206 steps through 232 possible outputs before it overflows and restarts. The corresponding output wave frequency is equal to the input clock frequency divided by 232. If M=2, then the phase register 1708 “rolls over” twice as fast, and the output frequency is doubled. This can be generalized as follows.

For a phase accumulator 4206 configured to accumulate n-bits (n generally ranges from 24 to 32 in most DDS systems, but as previously discussed n may be selected from a wide range of options), there are 2^(n) possible phase points. The digital word in the delta phase register, M, represents the amount the phase accumulator is incremented per clock cycle. If f_(c) is the clock frequency, then the frequency of the output sinewave is equal to:

$f_{0} = \frac{M \cdot f_{c}}{2^{n}}$

The above equation is known as the DDS “tuning equation.” Note that the frequency resolution of the system is equal to

$\frac{f_{0}}{2^{n}}.$ For n=32, the resolution is greater than one part in four billion. In one aspect of the DDS circuit 4200, not all of the bits out of the phase accumulator 4206 are passed on to the lookup table 4210, but are truncated, leaving only the first 13 to 15 most significant bits (MSBs), for example. This reduces the size of the lookup table 4210 and does not affect the frequency resolution. The phase truncation only adds a small but acceptable amount of phase noise to the final output.

The electrical signal waveform may be characterized by a current, voltage, or power at a predetermined frequency. Further, where any one of the surgical instruments of surgical system 1000 comprises ultrasonic components, the electrical signal waveform may be configured to drive at least two vibration modes of an ultrasonic transducer of the at least one surgical instrument. Accordingly, the generator circuit may be configured to provide an electrical signal waveform to at least one surgical instrument wherein the electrical signal waveform is characterized by a predetermined wave shape stored in the lookup table 4210 (or lookup table 4104 FIG. 13). Further, the electrical signal waveform may be a combination of two or more wave shapes. The lookup table 4210 may comprise information associated with a plurality of wave shapes. In one aspect or example, the lookup table 4210 may be generated by the DDS circuit 4200 and may be referred to as a direct digital synthesis table. DDS works by first storing a large repetitive waveform in onboard memory. A cycle of a waveform (sine, triangle, square, arbitrary) can be represented by a predetermined number of phase points as shown in TABLE 1 and stored into memory. Once the waveform is stored into memory, it can be generated at very precise frequencies. The direct digital synthesis table may be stored in a non-volatile memory of the generator circuit and/or may be implemented with a FPGA circuit in the generator circuit. The lookup table 4210 may be addressed by any suitable technique that is convenient for categorizing wave shapes. According to one aspect, the lookup table 4210 is addressed according to a frequency of the electrical signal waveform. Additionally, the information associated with the plurality of wave shapes may be stored as digital information in a memory or as part of the lookup table 4210.

In one aspect, the generator circuit may be configured to provide electrical signal waveforms to at least two surgical instruments simultaneously. The generator circuit also may be configured to provide the electrical signal waveform, which may be characterized two or more wave shapes, via an output channel of the generator circuit to the two surgical instruments simultaneously. For example, in one aspect the electrical signal waveform comprises a first electrical signal to drive an ultrasonic transducer (e.g., ultrasonic drive signal), a second RF drive signal, and/or a combination thereof. In addition, an electrical signal waveform may comprise a plurality of ultrasonic drive signals, a plurality of RF drive signals, and/or a combination of a plurality of ultrasonic and RF drive signals.

In addition, a method of operating the generator circuit according to the present disclosure comprises generating an electrical signal waveform and providing the generated electrical signal waveform to any one of the surgical instruments of surgical system 1000, where generating the electrical signal waveform comprises receiving information associated with the electrical signal waveform from a memory. The generated electrical signal waveform comprises at least one wave shape. Furthermore, providing the generated electrical signal waveform to the at least one surgical instrument comprises providing the electrical signal waveform to at least two surgical instruments simultaneously.

The generator circuit as described herein may allow for the generation of various types of direct digital synthesis tables. Examples of wave shapes for RF/Electrosurgery signals suitable for treating a variety of tissue generated by the generator circuit include RF signals with a high crest factor (which may be used for surface coagulation in RF mode), a low crest factor RF signals (which may be used for deeper tissue penetration), and waveforms that promote efficient touch-up coagulation. The generator circuit also may generate multiple wave shapes employing a direct digital synthesis lookup table 4210 and, on the fly, can switch between particular wave shapes based on the desired tissue effect. Switching may be based on tissue impedance and/or other factors.

In addition to traditional sine/cosine wave shapes, the generator circuit may be configured to generate wave shape(s) that maximize the power into tissue per cycle (i.e., trapezoidal or square wave). The generator circuit may provide wave shape(s) that are synchronized to maximize the power delivered to the load when driving RF and ultrasonic signals simultaneously and to maintain ultrasonic frequency lock, provided that the generator circuit includes a circuit topology that enables simultaneously driving RF and ultrasonic signals. Further, custom wave shapes specific to instruments and their tissue effects can be stored in a non-volatile memory (NVM) or an instrument EEPROM and can be fetched upon connecting any one of the surgical instruments of surgical system 1000 to the generator circuit.

The DDS circuit 4200 may comprise multiple lookup tables 4104 where the lookup table 4210 stores a waveform represented by a predetermined number of phase points (also may be referred to as samples), wherein the phase points define a predetermined shape of the waveform. Thus multiple waveforms having a unique shape can be stored in multiple lookup tables 4210 to provide different tissue treatments based on instrument settings or tissue feedback. Examples of waveforms include high crest factor RF electrical signal waveforms for surface tissue coagulation, low crest factor RF electrical signal waveform for deeper tissue penetration, and electrical signal waveforms that promote efficient touch-up coagulation. In one aspect, the DDS circuit 4200 can create multiple wave shape lookup tables 4210 and during a tissue treatment procedure (e.g., “on-the-fly” or in virtual real time based on user or sensor inputs) switch between different wave shapes stored in different lookup tables 4210 based on the tissue effect desired and/or tissue feedback. Accordingly, switching between wave shapes can be based on tissue impedance and other factors, for example. In other aspects, the lookup tables 4210 can store electrical signal waveforms shaped to maximize the power delivered into the tissue per cycle (i.e., trapezoidal or square wave). In other aspects, the lookup tables 4210 can store wave shapes synchronized in such way that they make maximizing power delivery by any one of the surgical instruments of surgical system 1000 when delivering RF and ultrasonic drive signals. In yet other aspects, the lookup tables 4210 can store electrical signal waveforms to drive ultrasonic and RF therapeutic, and/or sub-therapeutic, energy simultaneously while maintaining ultrasonic frequency lock. Generally, the output wave shape may be in the form of a sine wave, cosine wave, pulse wave, square wave, and the like. Nevertheless, the more complex and custom wave shapes specific to different instruments and their tissue effects can be stored in the non-volatile memory of the generator circuit or in the non-volatile memory (e.g., EEPROM) of the surgical instrument and be fetched upon connecting the surgical instrument to the generator circuit. One example of a custom wave shape is an exponentially damped sinusoid as used in many high crest factor “coagulation” waveforms, as shown in FIG. 43.

FIG. 15 illustrates one cycle of a discrete time digital electrical signal waveform 4300, in accordance with at least one aspect of the present disclosure of an analog waveform 4304 (shown superimposed over the discrete time digital electrical signal waveform 4300 for comparison purposes). The horizontal axis represents Time (t) and the vertical axis represents digital phase points. The digital electrical signal waveform 4300 is a digital discrete time version of the desired analog waveform 4304, for example. The digital electrical signal waveform 4300 is generated by storing an amplitude phase point 4302 that represents the amplitude per clock cycle T_(clk) over one cycle or period T₀. The digital electrical signal waveform 4300 is generated over one period T₀ by any suitable digital processing circuit. The amplitude phase points are digital words stored in a memory circuit. In the example illustrated in FIGS. 13, 14, the digital word is a six-bit word that is capable of storing the amplitude phase points with a resolution of 26 or 64 bits. It will be appreciated that the examples shown in FIGS. 13, 14 is for illustrative purposes and in actual implementations the resolution can be much higher. The digital amplitude phase points 4302 over one cycle T₀ are stored in the memory as a string of string words in a lookup table 4104, 4210 as described in connection with FIGS. 13, 14, for example. To generate the analog version of the analog waveform 4304, the amplitude phase points 4302 are read sequentially from the memory from 0 to T₀ per clock cycle T_(clk) and are converted by a DAC circuit 4108, 4212, also described in connection with FIGS. 13, 14. Additional cycles can be generated by repeatedly reading the amplitude phase points 4302 of the digital electrical signal waveform 4300 the from 0 to T₀ for as many cycles or periods as may be desired. The smooth analog version of the analog waveform 4304 is achieved by filtering the output of the DAC circuit 4108, 4212 by a filter 4112, 4214 (FIGS. 13 and 14). The filtered analog output signal 4114, 4222 (FIGS. 13 and 14) is applied to the input of a power amplifier.

FIG. 16 is a diagram of a control system 12950 that may be implemented as a nested PID feedback controller. A PID controller is a control loop feedback mechanism (controller) to continuously calculate an error value as the difference between a desired set point and a measured process variable and applies a correction based on proportional, integral, and derivative terms (sometimes denoted P, I, and D respectively). The nested PID controller feedback control system 12950 includes a primary controller 12952, in a primary (outer) feedback loop 12954 and a secondary controller 12955 in a secondary (inner) feedback loop 12956. The primary controller 12952 may be a PID controller 12972 as shown in FIG. 17, and the secondary controller 12955 also may be a PID controller 12972 as shown in FIG. 17. The primary controller 12952 controls a primary process 12958 and the secondary controller 12955 controls a secondary process 12960. The output 12966 of the primary process 12958 is subtracted from a primary set point SP₁ by a first summer 12962. The first summer 12962 produces a single sum output signal which is applied to the primary controller 12952. The output of the primary controller 12952 is the secondary set point SP₂. The output 12968 of the secondary process 12960 is subtracted from the secondary set point SP₂ by a second summer 12964.

FIG. 17 illustrates a PID feedback control system 12970 according to one aspect of this disclosure. The primary controller 12952 or the secondary controller 12955, or both, may be implemented as a PID controller 12972. In one aspect, the PID controller 12972 may comprise a proportional element 12974 (P), an integral element 12976 (I), and a derivative element 12978 (D). The outputs of the P, I, D elements 12974, 12976, 12978 are summed by a summer 12986, which provides the control variable μ(t) to the process 12980. The output of the process 12980 is the process variable y(t). A summer 12984 calculates the difference between a desired set point r(t) and a measured process variable y(t), received by beedback loop 12982. The PID controller 12972 continuously calculates an error value e(t) (e.g., difference between closure force threshold and measured closure force) as the difference between a desired set point r(t) (e.g., closure force threshold) and a measured process variable y(t) (e.g., velocity and direction of closure tube) and applies a correction based on the proportional, integral, and derivative terms calculated by the proportional element 12974 (P), integral element 12976 (I), and derivative element 12978 (D), respectively. The PID controller 12972 attempts to minimize the error e(t) over time by adjustment of the control variable p(t) (e.g., velocity and direction of the closure tube).

In accordance with the PID algorithm, the “P” element 12974 accounts for present values of the error. For example, if the error is large and positive, the control output will also be large and positive. In accordance with the present disclosure, the error term e(t) is the different between the desired closure force and the measured closure force of the closure tube. The “I” element 12976 accounts for past values of the error. For example, if the current output is not sufficiently strong, the integral of the error will accumulate over time, and the controller will respond by applying a stronger action. The “D” element 12978 accounts for possible future trends of the error, based on its current rate of change. For example, continuing the P example above, when the large positive control output succeeds in bringing the error closer to zero, it also puts the process on a path to large negative error in the near future. In this case, the derivative turns negative and the D module reduces the strength of the action to prevent this overshoot.

It will be appreciated that other variables and set points may be monitored and controlled in accordance with the feedback control systems 12950, 12970. For example, the adaptive closure member velocity control algorithm described herein may measure at least two of the following parameters: firing member stroke location, firing member load, displacement of cutting element, velocity of cutting element, closure tube stroke location, closure tube load, among others.

FIG. 18 is an alternative system 132000 for controlling the frequency of an ultrasonic electromechanical system 132002 and detecting the impedance thereof, in accordance with at least one aspect of the present disclosure. The system 132000 may be incorporated into a generator. A processor 132004 coupled to a memory 132026 programs a programmable counter 132006 to tune to the output frequency f₀ of the ultrasonic electromechanical system 132002. The input frequency is generated by a crystal oscillator 132008 and is input into a fixed counter 132010 to scale the frequency to a suitable value. The outputs of the fixed counter 132010 and the programmable counter 132006 are applied to a phase/frequency detector 132012. The output of the phase/frequency detector 132012 is applied to an amplifier/active filter circuit 132014 to generate a tuning voltage V_(t) that is applied to a voltage controlled oscillator 132016 (VCO). The VCO 132016 applies the output frequency f₀ to an ultrasonic transducer portion of the ultrasonic electromechanical system 132002, shown here modeled as an equivalent electrical circuit. The voltage and current signals applied to the ultrasonic transducer are monitored by a voltage sensor 132018 and a current sensor 132020.

The outputs of the voltage and current sensors 132018, 132020 are applied to another phase/frequency detector 132022 to determine the phase angle between the voltage and current as measured by the voltage and current sensors 132018, 132020. The output of the phase/frequency detector 132022 is applied to one channel of a high speed analog to digital converter 132024 (ADC) and is provided to the processor 132004 therethrough. Optionally, the outputs of the voltage and current sensors 132018, 132020 may be applied to respective channels of the two-channel ADC 132024 and provided to the processor 132004 for zero crossing, FFT, or other algorithm described herein for determining the phase angle between the voltage and current signals applied to the ultrasonic electromechanical system 132002.

Optionally the tuning voltage V_(t), which is proportional to the output frequency f₀, may be fed back to the processor 132004 via the ADC 132024. This provides the processor 132004 with a feedback signal proportional to the output frequency f₀ and can use this feedback to adjust and control the output frequency f₀.

Estimating the State of the Jaw (Pad Burn Through, Staples, Broken Blade, Bone In Jaw, Tissue In Jaw)

A challenge with ultrasonic energy delivery is that ultrasonic acoustics applied on the wrong materials or the wrong tissue can result in device failure, for example, clamp arm pad burn through or ultrasonic blade breakage. It is also desirable to detect what is located in the jaws of an end effector of an ultrasonic device and the state of the jaws without adding additional sensors in the jaws. Locating sensors in the jaws of an ultrasonic end effector poses reliability, cost, and complexity challenges.

Ultrasonic spectroscopy smart blade algorithm techniques may be employed for estimating the state of the jaw (clamp arm pad burn through, staples, broken blade, bone in jaw, tissue in jaw, back-cutting with jaw closed, etc.) based on the impedance

${Z_{g}(t)} = \frac{V_{g}(t)}{I_{g}(t)}$ of an ultrasonic transducer configured to drive an ultrasonic transducer blade, in accordance with at least one aspect of the present disclosure. The impedance Z_(g)(t), magnitude |Z|, and phase φ are plotted as a function of frequency f.

Dynamic mechanical analysis (DMA), also known as dynamic mechanical spectroscopy or simply mechanical spectroscopy, is a technique used to study and characterize materials. A sinusoidal stress is applied to a material, and the strain in the material is measured, allowing the determination of the complex modulus of the material. The spectroscopy as applied to ultrasonic devices includes exciting the tip of the ultrasonic blade with a sweep of frequencies (compound signals or traditional frequency sweeps) and measuring the resulting complex impedance at each frequency. The complex impedance measurements of the ultrasonic transducer across a range of frequencies are used in a classifier or model to infer the characteristics of the ultrasonic end effector. In one aspect, the present disclosure provides a technique for determining the state of an ultrasonic end effector (clamp arm, jaw) to drive automation in the ultrasonic device (such as disabling power to protect the device, executing adaptive algorithms, retrieving information, identifying tissue, etc.).

FIG. 19 is a spectra 132030 of an ultrasonic device with a variety of different states and conditions of the end effector where the impedance Z_(g)(t), magnitude |Z|, and phase φ are plotted as a function of frequency f, in accordance with at least one aspect of the present disclosure. The spectra 132030 is plotted in three-dimensional space where frequency (Hz) is plotted along the x-axis, phase (Rad) is plotted along the y-axis, and magnitude (Ohms) is plotted along the z-axis.

Spectral analysis of different jaw bites and device states produces different complex impedance characteristic patterns (fingerprints) across a range of frequencies for different conditions and states. Each state or condition has a different characteristic pattern in 3D space when plotted. These characteristic patterns can be used to estimate the condition and state of the end effector. FIG. 19 shows the spectra for air 132032, clamp arm pad 132034, chamois 132036, staple 132038, and broken blade 132040. The chamois 132036 may be used to characterize different types of tissue.

The spectra 132030 can be evaluated by applying a low-power electrical signal across the ultrasonic transducer to produce a non-therapeutic excitation of the ultrasonic blade. The low-power electrical signal can be applied in the form of a sweep or a compound Fourier series to measure the impedance

${Z_{g}(t)} = \frac{V_{g}(t)}{I_{g}(t)}$ across the ultrasonic transducer at a range of frequencies in series (sweep) or in parallel (compound signal) using an FFT.

Methods of Classification of New Data

For each characteristic pattern, a parametric line can be fit to the data used for training using a polynomial, a Fourier series, or any other form of parametric equation as may be dictated by convenience. A new data point is then received and is classified by using the Euclidean perpendicular distance from the new data point to the trajectory that has been fitted to the characteristic pattern training data. The perpendicular distance of the new data point to each of the trajectories (each trajectory representing a different state or condition) is used to assign the point to a state or condition.

The probability distribution of distance of each point in the training data to the fitted curve can be used to estimate the probability of a correctly classified new data point. This essentially constructs a two-dimensional probability distribution in a plane perpendicular to the fitted trajectory at each new data point of the fitted trajectory. The new data point can then be included in the training set based on its probability of correct classification to make an adaptive, learning classifier that readily detects high-frequency changes in states but adapts to slow occurring deviations in system performance, such as a device getting dirty or the pad wearing out.

FIG. 20 is a graphical representation of a plot 132042 of a set of 3D training data set (S), where ultrasonic transducer impedance Z_(g)(t), magnitude |Z|, and phase φ are plotted as a function of frequency f, in accordance with at least one aspect of the present disclosure. The 3D training data set (S) plot 132042 is graphically depicted in three-dimensional space where phase (Rad) is plotted along the x-axis, frequency (Hz) is plotted along the y-axis, magnitude (Ohms) is plotted along the z-axis, and a parametric Fourier series is fit to the 3D training data set (S). A methodology for classifying data is based on the 3D training data set (S0 is used to generate the plot 132042).

The parametric Fourier series fit to the 3D training data set (S) is defined by:

$\overset{\rightarrow}{p} = {{\overset{\rightarrow}{a}}_{0} + {\sum\limits_{n = 1}^{\infty}\left( {{{\overset{\rightarrow}{a}}_{n}\mspace{11mu}\cos\mspace{11mu}\frac{n\;\pi\; t}{L}} + {{\overset{\rightarrow}{b}}_{n}\mspace{11mu}\sin\mspace{11mu}\frac{n\;\pi\; t}{L}}} \right)}}$

For a new point {right arrow over (z)}, the perpendicular distance from {right arrow over (p)} to {right arrow over (z)} is found by:

$\begin{matrix} {D = {{\overset{\rightarrow}{p} - \overset{\rightarrow}{z}}}} & \; \\ {{When}\text{:}} & \; \\ {\frac{\partial D}{\partial T} = 0} & \; \\ {{Then}\text{:}} & \; \\ {D = D_{\bot}} & \; \end{matrix}$

A probability distribution of D can be used to estimate the probability of a data point {right arrow over (z)} belonging to the group S.

Control

Based on the classification of data measured before, during, or after activation of the ultrasonic transducer/ultrasonic blade, a variety of automated tasks and safety measures can be implemented. Similarly, the state of the tissue located in the end effector and temperature of the ultrasonic blade also can be inferred to some degree, and used to better inform the user of the state of the ultrasonic device or protect critical structures, etc. Temperature control of an ultrasonic blade is described in commonly owned U.S. Provisional Patent Application No. 62/640,417, filed Mar. 8, 2018, titled TEMPERATURE CONTROL IN ULTRASONIC DEVICE AND CONTROL SYSTEM THEREFOR, which is incorporated herein by reference in its entirety.

Similarly, power delivery can be reduced when there is a high probability that the ultrasonic blade is contacting the clamp arm pad (e.g., without tissue in between) or if there is a probability that the ultrasonic blade has broken or that the ultrasonic blade is touching metal (e.g., a staple). Furthermore, back-cutting can be disallowed if the jaw is closed and no tissue is detected between the ultrasonic blade and the clamp arm pad.

Integration of Other Data to Improve Classification

This system can be used in conjunction with other information provided by sensors, the user, metrics on the patient, environmental factors, etc., by combing the data from this process with the aforementioned data using probability functions and a Kalman filter. The Kalman filter determines the maximum likelihood of a state or condition occurring given a plethora of uncertain measurements of varying confidence. Since this method allows for an assignment of probability to a newly classified data point, this algorithm's information can be implemented with other measures or estimates in a Kalman filter.

FIG. 21 is a logic flow diagram 132044 depicting a control program or a logic configuration to determine jaw conditions based on the complex impedance characteristic pattern (fingerprint), in accordance with at least one aspect of the present disclosure. Prior to determining jaw conditions based on the complex impedance characteristic pattern (fingerprint), a database is populated with reference complex impedance characteristic patterns or a training data sets (S) that characterize various jaw conditions, including, without limitation, air 132032, clamp arm pad 132034, chamois 132036, staple 132038, broken blade 132040, as shown in FIG. 82, and a variety of tissue types and conditions. The chamois dry or wet, full byte or tip, may be used to characterize different types of tissue. The data points used to generate reference complex impedance characteristic patterns or a training data set (S) are obtained by applying a sub-therapeutic drive signal to the ultrasonic transducer, sweeping the driving frequency over a predetermined range of frequencies from below resonance to above resonance, measuring the complex impedance at each of the frequencies, and recording the data points. The data points are then fit to a curve using a variety of numerical methods including polynomial curve fit, Fourier series, and/or parametric equation. A parametric Fourier series fit to the reference complex impedance characteristic patterns or a training data set (S) is described herein.

Once the reference complex impedance characteristic patterns or a training data sets (S) are generated, the ultrasonic instrument measures new data points, classifies the new points, and determines whether the new data points should be added to the reference complex impedance characteristic patterns or a training data sets (S).

Turning now to the logic flow diagram of FIG. 21, in one aspect, the control circuit measures 132046 a complex impedance of an ultrasonic transducer, wherein the complex impedance is defined as

${Z_{g}(t)} = {\frac{V_{g}(t)}{I_{g}(t)}.}$ The control circuit receives 132048 a complex impedance measurement data point and compares 132050 the complex impedance measurement data point to a data point in a reference complex impedance characteristic pattern. The control circuit classifies 132052 the complex impedance measurement data point based on a result of the comparison analysis and assigns 132054 a state or condition of the end effector based on the result of the comparison analysis.

In one aspect, the control circuit receives the reference complex impedance characteristic pattern from a database or memory coupled to the processor. In one aspect, the control circuit generates the reference complex impedance characteristic pattern as follows. A drive circuit coupled to the control circuit applies a nontherapeutic drive signal to the ultrasonic transducer starting at an initial frequency, ending at a final frequency, and at a plurality of frequencies therebetween. The control circuit measures the impedance of the ultrasonic transducer at each frequency and stores a data point corresponding to each impedance measurement. The control circuit curve fits a plurality of data points to generate a three-dimensional curve of representative of the reference complex impedance characteristic pattern, wherein the magnitude |Z| and phase φ are plotted as a function of frequency f. The curve fitting includes a polynomial curve fit, a Fourier series, and/or a parametric equation.

In one aspect, the control circuit receives a new impedance measurement data point and classifies the new impedance measurement data point using a Euclidean perpendicular distance from the new impedance measurement data point to a trajectory that has been fitted to the reference complex impedance characteristic pattern. The control circuit estimates a probability that the new impedance measurement data point is correctly classified. The control circuit adds the new impedance measurement data point to the reference complex impedance characteristic pattern based on the probability of the estimated correct classification of the new impedance measurement data point. In one aspect, the control circuit classifies data based on a training data set (S), where the training data set (S) comprises a plurality of complex impedance measurement data, and curve fits the training data set (S) using a parametric Fourier series, wherein S is defined herein and wherein the probability distribution is used to estimate the probability of the new impedance measurement data point belonging to the group S.

State of Jaw Classifier Based on Model

There has been an existing interest in classifying matter located within the jaws of an ultrasonic device including tissue types and condition. In various aspects, it can be shown that with high data sampling and sophisticated pattern recognition this classification is possible. The approach is based on impedance as a function of frequency, where magnitude, phase, and frequency are plotted in 3D the patterns look like ribbons as shown in FIGS. 19 and 20 and the logic flow diagram of FIG. 21. This disclosure provides an alternative smart blade algorithm approach that is based on a well-established model for piezoelectric transducers.

By way of example, the equivalent electrical lumped parameter model is known to be an accurate model of the physical piezoelectric transducer. It is based on the Mittag-Leffler expansion of a tangent near a mechanical resonance. When the complex impedance or the complex admittance is plotted as an imaginary component versus a real component, circles are formed. FIG. 22 is a circle plot 132056 of complex impedance plotted as an imaginary component versus real components of a piezoelectric vibrator, in accordance with at least one aspect of the present disclosure. FIG. 23 is a circle plot 132058 of complex admittance plotted as an imaginary component versus real components of a piezoelectric vibrator, in accordance with at least one aspect of the present disclosure. The circles depicted in FIGS. 22 and 23 are taken from the IEEE 177 Standard, which is incorporated herein by reference in its entirety. Tables 1-4 are taken from the IEEE 177 Standard and disclosed herein for completeness.

The circle is created as the frequency is swept from below resonance to above resonance. Rather than stretching the circle out in 3D, a circle is identified and the radius (r) and offsets (a, b) of the circle are estimated. These values are then compared with established values for given conditions. These conditions may be: 1) open nothing in jaws, 2) tip bite 3) full bite and staple in jaws. If the sweep generates multiple resonances, circles of different characteristics will be present for each resonance. Each circle will be drawn out before the next if the resonances are separated. Rather than fitting a 3D curve with a series approximation, the data is fitted with a circle. The radius (r) and offsets (a, b) can be calculated using a processor programmed to execute a variety of mathematical or numerical techniques described below. These values may be estimated by capturing an image of a circle and, using image processing techniques, the radius (r) and offsets (a, b) that define the circle are estimated.

FIG. 24 is a circle plot 132060 of complex admittance for a 55.5 kHz ultrasonic piezoelectric transducer for lumped parameters inputs and outputs specified hereinbelow. Values for a lumped parameter model were used to generate the complex admittance. A moderate load was applied in the model. The obtained admittance circle generated in MathCad is shown in FIG. 24. The circle plot 132060 is formed when the frequency is swept from 54 to 58 kHz.

The lumped parameter input values are: Co=3.0 nF Cs=8.22 pF Ls=1.0 H Rs=450Ω

The outputs of the model based on the inputs are:

$\begin{matrix} {{am} = {\frac{{D \cdot C} - {B \cdot C}}{{A \cdot C} - B^{2}} = {1.013 \cdot 10^{3}}}} \\ {{bm} = {\frac{{A \cdot E} - {B \cdot D}}{{A \cdot C} - B^{2}} = {- 954.585}}} \\ {{rm} = {{\frac{1}{fpts}\left( {\sum\limits_{i}^{fpts}\sqrt[2]{\left( {\left( {{Zout}_{1,i} = {am}} \right)^{2} + \left( {{Zout}_{2,i} - {bm}} \right)^{2}} \right)}} \right)} = {1.012 \cdot 10^{3}}}} \end{matrix}$

The output values are used to plot the circle plot 132060 shown in FIG. 24. The circle plot 132060 has a radius (r) and the center 132062 is offset (a, b) from the origin 132064 as follows: r=1.012*10³ a=1.013*10³ b=−954.585

The summations A-E specified below are needed to estimate the circle plot 132060 plot for the example given in FIG. 24, in accordance with at least one aspect of the present disclosure. Several algorithms exist to calculate a fit to a circle. A circle is defined by its radius (r) and offsets (a, b) of the center from the origin: r ²=(x−a)²+(y−b)²

The modified least squares method (Umbach and Jones) is convenient in that there a simple close formed solution for a, b, and r.

$\hat{a} = \frac{{DC} - {BE}}{{AC} - B^{2}}$ $\hat{b} = \frac{{AE} - {BD}}{{AC} - B^{2}}$ $\hat{r} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}\sqrt{\left( {x_{i} - \hat{a}} \right)^{2} + \left( {y_{i} - \hat{b}} \right)^{2}}}}$

The caret symbol over the variable “a” indicates an estimate of the true value. A, B, C, D, and E are summations of various products which are calculated from the data. They are included herein for completeness as follows:

$\mspace{20mu}{A:={{{{fpts} \cdot {\sum\limits_{i}^{fpts}\left( {Zout}_{1,i} \right)^{2}}} - \left( {\sum\limits_{i}^{fpts}\left( {Zout}_{1,i} \right)} \right)^{2}} = {5.463 \cdot 10^{10}}}}$ $B:={{{{fpts}{\sum\limits_{i}^{fpts}\left( {{Zout}_{1,i} \cdot {Zout}_{2,i}} \right)}} - \left( {\left( {\sum\limits_{i}^{fpts}\left( {Zout}_{1,i} \right)} \right) \cdot \left( {\sum\limits_{i}^{fpts}\left( {Zout}_{2,i} \right)} \right)} \right)} = {5.461 \cdot 10^{7}}}$ $\mspace{20mu}{C:={{{{fpts}{\sum\limits_{i}^{fpts}\left( {Zout}_{2,i} \right)^{2}}} - \left( {\sum\limits_{i}^{fpts}\left( {Zout}_{2,i} \right)} \right)^{2}} = {5.445 \cdot 10^{10}}}}$ $D:={{0.5 \cdot \left( {{{fpts}{\sum\limits_{i}^{fpts}\left( {{Zout}_{1,i} \cdot \left( {Zout}_{2,i} \right)^{2}} \right)}} - {\left( {\sum\limits_{i}^{fpts}\left( {Zout}_{1,i} \right)} \right) \cdot \left( {\sum\limits_{i}^{fpts}\left( {Zout}_{2,i} \right)^{2}} \right)} + {{fpts}{\sum\limits_{i}^{fpts}\left( {Zout}_{1,i}^{3} \right)}} - {\left( {\sum\limits_{i}^{fpts}\left( {Zout}_{1,i} \right)} \right) \cdot \left( {\sum\limits_{i}^{fpts}\left( {Zout}_{1,i} \right)^{2}} \right)}} \right)} = {5.529 \cdot 10^{3}}}$ $E:={{0.5 \cdot \left( {{{fpts}{\sum\limits_{i}^{fpts}\left( {{Zout}_{2,i} \cdot \left( {Zout}_{1,i} \right)^{2}} \right)}} - {\left( {\sum\limits_{i}^{fpts}\left( {Zout}_{2,i} \right)} \right) \cdot \left( {\sum\limits_{i}^{fpts}\left( {Zout}_{1,i} \right)^{2}} \right)} + {{fpts}{\sum\limits_{i}^{fpts}\left( {Zout}_{2,i}^{3} \right)}} - {\left( {\sum\limits_{i}^{fpts}\left( {Zout}_{2,i} \right)} \right) \cdot \left( {\sum\limits_{i}^{fpts}\left( {Zout}_{2,i} \right)^{2}} \right)}} \right)} = {{- 5.192} \cdot 10^{13}}}$

Z1, i is a first vector of the real components referred to as conductance;

Z2,i is a second of the imaginary components referred to as susceptance; and

Z3,i is a third vector that represents the frequencies at which admittances are calculated.

This disclosure will work for ultrasonic systems and may possibly be applied to electrosurgical systems, even though electrosurgical systems do not rely on a resonance.

FIGS. 25-29 illustrate images taken from an impedance analyzer showing impedance/admittance circle plots for an ultrasonic device with the end effector jaw in various open or closed configurations and loading. The circle plots in solid line depict impedance and the circle plots in broken lines depict admittance, in accordance with at least one aspect of the present disclosure. By way of example, the impedance/admittance circle plots are generated by connecting an ultrasonic device to an impedance analyzer. The display of the impedance analyzer is set to complex impedance and complex admittance, which can be selectable from the front panel of the impedance analyzer. An initial display may be obtained with the jaw of the ultrasonic end effector in an open position and the ultrasonic device in an unloaded state, as described below in connection with FIG. 25, for example. The autoscale display function of the impedance analyzer may be used to generate both the complex impedance and admittance circle plots. The same display is used for subsequent runs of the ultrasonic device with different loading conditions as shown in the subsequent FIGS. 25-29. A LabVIEW application may be employed to upload the data files. In another technique, the display images may be captured with a camera, such as a smartphone camera, like an iPhone or Android. As such, the image of the display may include some “keystone-ing” and in general may not appear to be parallel to the screen. Using this technique, the circle plot traces on the display will appear distorted in the captured image. With this approach, the material located in the jaws of the ultrasonic end effector can be classified.

The complex impedance and complex admittance are just the reciprocal of one another. No new information should be added by looking at both. Another consideration includes determining how sensitive the estimates are to noise when using complex impedance or complex admittance.

In the examples illustrated in FIGS. 25-29, the impedance analyzer is set up with a range to just capture the main resonance. By scanning over a wider range of frequencies more resonances may be encountered and multiple circle plots may be formed. An equivalent circuit of an ultrasonic transducer may be modeled by a first “motional” branch having a serially connected inductance Ls, resistance Rs and capacitance Cs that define the electromechanical properties of the resonator, and a second capacitive branch having a static capacitance C0. In the impedance/admittance plots shown in FIGS. 25-29 that follow, the values of the components of the equivalent circuit are: Ls=L1=1.1068 H Rs=R1=311.352Ω Cs=C1=7.43265 pF C0=C0=3.64026 nF

The oscillator voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 Ω/div and the admittance (Y) scale is 500 μS/div. Measurements of values that may characterize the impedance (Z) and admittance (Y) circle plots may be obtained at the locations on the circle plots as indicated by an impedance cursor and an admittance cursor.

State of Jaw: Open With No Loading

FIG. 25 is a graphical display 132066 of an impedance analyzer showing complex impedance (Z)/admittance (Y) circle plots 132068, 132070 for an ultrasonic device with the jaw open and no loading where a circle plot 132068 in solid line depicts complex impedance and a circle plot 132070 in broken line depicts complex admittance, in accordance with at least one aspect of the present disclosure. The oscillator voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 Ω/div and the admittance (Y) scale is 500 μS/div. Measurements of values that may characterize the complex impedance (Z) and admittance (Y) circle plots 132068, 132070 may be obtained at locations on the circle plots 132068, 132070 as indicated by the impedance cursor 132072 and the admittance cursor 132074. Thus, the impedance cursor 132072 is located at a portion of the impedance circle plot 132068 that is equivalent to about 55.55 kHz, and the admittance cursor 132074 is located at a portion of the admittance circle plot 132070 that is equivalent to about 55.29 kHz. As depicted in FIG. 25, the position of the impedance cursor 132072 corresponds to values of: R=1.66026Ω X=−697.309Ω

Where R is the resistance (real value) and X is the reactance (imaginary value). Similarly, the position of the admittance cursor 132074 corresponds to values of: G=64.0322 μS B=1.63007 mS

Where G is the conductance (real value) and B is susceptance (imaginary value).

State Of Jaw: Clamped On Dry Chamois

FIG. 26 is a graphical display 132076 of an impedance analyzer showing complex impedance (Z)/admittance (Y) circle plots 132078, 132080 for an ultrasonic device with the jaw of the end effector clamped on dry chamois where the impedance circle plot 132078 is shown in solid line and the admittance circle plot 132080 is shown in broken line, in accordance with at least one aspect of the present disclosure. The voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 Ω/div and the admittance (Y) scale is 500 μS/div.

Measurements of values that may characterize the complex impedance (Z) and admittance (Y) circle pots 132078, 132080 may be obtained at locations on the circle plots 132078, 132080 as indicated by the impedance cursor 132082 and the admittance cursor 132084. Thus, the impedance cursor 132082 is located at a portion of the impedance circle plot 132078 that is equivalent to about 55.68 kHz, and the admittance cursor 132084 is located at a portion of the admittance circle plot 132080 that is equivalent to about 55.29 kHz. As depicted in FIG. 26, the position of the impedance cursor 132082 corresponds to values of: R=434.577Ω X=−758.772Ω

Where R is the resistance (real value) and X is the reactance (imaginary value).

Similarly, the position of the admittance cursor 132084 corresponds to values of: G=85.1712 μS B=1.49569 mS

Where G is the conductance (real value) and B is susceptance (imaginary value).

State Of Jaw: Tip Clamped On Moist Chamois

FIG. 27 is a graphical display 132086 of an impedance analyzer showing complex impedance (Z)/admittance (Y) circle plots 132098, 132090 for an ultrasonic device with the jaw tip clamped on moist chamois where the impedance circle plot 132088 is shown in solid line and the admittance circle plot 132090 is shown in broken line, in accordance with at least one aspect of the present disclosure. The voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 Ω/div and the admittance (Y) scale is 500 μS/div.

Measurements of values that may characterize the complex impedance (Z) and complex admittance (Y) circle plots 132088, 132090 may be obtained at locations on the circle plots 132088, 132090 as indicated by the impedance cursor 132092 and the admittance cursor 132094. Thus, the impedance cursor 132092 is located at a portion of the impedance circle plot 132088 that is equivalent to about 55.68 kHz, and the admittance cursor 132094 is located at a portion of the admittance circle plot 132090 that is equivalent to about 55.29 kHz. As depicted in FIG. 28, the impedance cursor 132092 corresponds to values of: R=445.259Ω X=−750.082Ω

Where R is the resistance (real value) and X is the reactance (imaginary value). Similarly, the admittance cursor 132094 corresponds to values of: G=96.2179 μS B=1.50236 mS

Where G is the conductance (real value) and B is susceptance (imaginary value).

State of Jaw: Fully Clamped on Moist Chamois

FIG. 28 is a graphical display 132096 of an impedance analyzer showing complex impedance (Z)/admittance (Y) circle plots 132098, 132100 for an ultrasonic device with the jaw fully clamped on moist chamois where the impedance circle plot 132098 is shown in solid line and the admittance circle plot 132100 is shown in broken line, in accordance with at least one aspect of the present disclosure. The voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 55 kHz to 56 kHz. The impedance (Z) scale is 200 Ω/div and the admittance (Y) scale is 500 μS/div.

Measurements of values that may characterize the impedance and admittance circle plots 132098, 132100 may be obtained at locations on the circle plots 132098, 1332100 as indicated by the impedance cursor 13212 and admittance cursor 132104. Thus, the impedance cursor 132102 is located at a portion of the impedance circle plot 132098 equivalent to about 55.63 kHz, and the admittance cursor 132104 is located at a portion of the admittance circle plot 132100 equivalent to about 55.29 kHz. As depicted in FIG. 28, the impedance cursor 132102 corresponds to values of R, the resistance (real value, not shown), and X, the reactance (imaginary value, also not shown).

Similarly, the admittance cursor 132104 corresponds to values of: G=137.272 μS B=1.48481 mS

Where G is the conductance (real value) and B is susceptance (imaginary value).

State of Jaw: Open With No Loading

FIG. 29 is a graphical display 132106 of an impedance analyzer showing impedance (Z)/admittance (Y) circle plots where frequency is swept from 48 kHz to 62 kHz to capture multiple resonances of an ultrasonic device with the jaw open and no loading where the area designated by the rectangle 132108 shown in broken line is to help see the impedance circle plots 132110 a, 132110 b, 132110 c shown in solid line and the admittance circle plots 132112 a, 132112 b, 132112 c, in accordance with at least one aspect of the present disclosure. The voltage applied to the ultrasonic transducer is 500 mV and the frequency is swept from 48 kHz to 62 kHz. The impedance (Z) scale is 500 Ω/div and the admittance (Y) scale is 500 μS/div.

Measurements of values that may characterize the impedance and admittance circle plots 132110 a-c, 132112 a-c may be obtained at locations on the impedance and admittance circle plots 132110 a-c, 132112 a-c as indicated by the impedance cursor 132114 and the admittance cursor 132116. Thus, the impedance cursor 132114 is located at a portion of the impedance circle plots 132110 a-c equivalent to about 55.52 kHz, and the admittance cursor 132116 is located at a portion of the admittance circle plot 132112 a-c equivalent to about 59.55 kHz. As depicted in FIG. 29, the impedance cursor 132114 corresponds to values of: R=1.86163 kΩ X=−536.229Ω

Where R is the resistance (real value) and X is the reactance (imaginary value). Similarly, the admittance cursor 132116 corresponds to values of: G=649.956 μS B=2.51975 mS

Where G is the conductance (real value) and B is susceptance (imaginary value).

Because there are only 400 samples across the sweep range of the impedance analyzer, there are only a few points about a resonance. So, the circle on the right side becomes choppy. But this is only due to the impedance analyzer and the settings used to cover multiple resonances.

When multiple resonances are present, there is more information to improve the classifier. The circle plots 132110 a-c, 132112 a-c fit can be calculated for each as encountered to keep the algorithm running fast. So once there is a cross of the complex admittance, which implies a circle, during the sweep, a fit can be calculated.

Benefits include in-the-jaw classifier based on data and a well-known model for ultrasonic systems. Count and characterizations of circles are well known in vision systems. So data processing is readily available. For example, a closed form solution exists to calculate the radius and axes' offsets for a circle. This technique can be relatively fast.

TABLE 2 is a list of symbols used for lumped parameter model of a piezoelectric transducer (from IEEE 177 Standard).

TABLE 2 References Symbols Meaning SI Units Equations Tables Figures B_(p) Equivalent parallel mho 2 susceptance of vibrator C_(o) Shunt (parallel) capacitance farad 2, 3, 4, 8 5 1, 4 in the equivalent electric circuit C₁ Motional capacitance in the farad 2, 3, 4, 6, 5 1, 4 equivalent electric circuit 8, 9 f Frequency hertz 3 f_(a) Antiresonance frequency, hertz 2, 4 2, 3 zero susceptance f_(m) Frequency of maximum hertz 2, 4 2, 3 admittance (minimum impedance) f_(n) Frequency of minimum hertz 2, 4 2, 3 admittance (maximum impedance) f_(p) Parallel resonance frequency hertz 2, 3 2, 4 2 $({lossless}) = \frac{1}{2\;\pi\sqrt{L_{1}\frac{C_{1}C_{O}}{C_{1} + C_{O}}}}$ f_(r) Resonance frequency, zero hertz 2, 4 2, 3 substance f_(B) Motional (series) resonance hertz 2, 3, 6, 7, 2, 4 2, 3, 6, 8 frequency 1/2 9, 11a, 11b, 11c, 12, G_(p) Equivalent parallel 1 conductance of vibrator L₁ Motional inductance in the henry 8, 9 1, 4, 5 equivalent electric circuit M $\begin{matrix} {{{Figure}\mspace{14mu}{of}\mspace{14mu}{merit}\mspace{14mu}{of}\mspace{14mu} a\mspace{14mu}{vibrator}} = \frac{Q}{r}} \\ {M = \frac{1}{\omega_{s}C_{O}R_{1}}} \end{matrix}\quad$ dimensionless 10, 11a, 11b 3, 4, 5 Q $\quad\begin{matrix} {{{Quality}\mspace{14mu}{factor}\mspace{14mu} Q} = {\frac{\omega_{s}L_{1}}{R_{1}} =}} \\ {\frac{1}{\omega_{s}C_{1}R_{1}} = {rM}} \end{matrix}$ dimensionless 12 3 6, 8 r ${{Capacitance}\mspace{14mu}{ratio}\mspace{14mu} r} = \frac{C_{o}}{C_{1}}$ dimensionless 2, 3, 10, 11 2, 3, 4, 5 8 R_(a) Impedance at zero phase ohm 2, 3 angle near antiresonance R_(e) Equivalent series resistance ohm 1, 2 of vibrator R_(r) Impedance at f_(r) zero phase ohm 2, 3 angle R₁ Motional resistance in the ohm 4, 8, 10, 2,5 1, 3, 4, equivalent electric circuit 11a, 11b, 6, 7, 8 11c, 12 X_(e) Equivalent series reactance of ohm 1,2 vibrator X_(o) Reactance of shunt (parallel) ohm 1, 4, 5 5 3, 7 capacitance at series ${resonance} = \frac{1}{\omega_{s}C_{o}}$ X₁ Reactance of motional ohm 2 2 (series) arm of vibratorX₁ = ${{}_{}^{}{}_{}^{}} - \frac{1}{{}_{}^{}{}_{}^{}}$ Y Admittance of vibrator mho 1 $Y = {{G_{p} + {{}_{}^{}{}_{}^{}}} = \frac{1}{z}}$ Y_(m) Maximum admittance of mho 3 vibrator Y_(n) Minimum admittance of mho 3 vibrator Z Impedance of vibrator ohm 1 Z = Re +_(j)X_(e) Z_(m) Minimum impedance of ohm 3 vibrator Z_(n) Maximum impedance of ohm 3 vibrator Absolute value of impedance ohm 2 2 ${{of}\mspace{14mu}{vibrator}\mspace{14mu} Z} = \sqrt{R_{e}^{2} + X_{e}^{2}}$ Absolute value of impedance ohm 2 at f_(m) (minimum impedance) Absolute value of impedance ohm 2 at f_(n) (maximum impedance) δ Normalized damping dimensionless 1 2 factorδ = _(ω)C_(o)R₁ Ω Normalized frequency dimensionless 1 2 ${{factor}\mspace{11mu}\Omega} = \frac{f^{2} - f_{s}^{2}}{f_{p}^{2} - f_{s}^{2}}$ ω Circular (angular) frequency hertz 2 ω = 2πf ω_(s) Circular frequency at motional hertz resonance ω_(s) = 2πf TABLE 3 is a list of symbols for the transmission network (from IEEE 177 Standard).

TABLE 3 References Symbols Meaning SI Units Equations Tables Figures b Normalized compensation ${{factor}\; 1} - \frac{1}{4\;\pi^{2}f_{s}^{2}C_{O}L_{O}}$ dimensionless 4, 10 5 B Normalized admittance factor dimensionless 10 5 C Normalized admittance factor dimensionless 10 5 C_(A-B) Stray capacitance between farad the terminals A-B (FIG. 4) C_(L) Load capacitance farad 6 4 C_(T) Shunt capacitance terminating transmission farad 4, 10 5 4 circuit C_(L1) Load capacitance farad 7 C_(L2) Load capacitance farad 7 e₂ Output voltage of volt 4 transmission network f_(mT) Frequency of maximum hertz 10 transmission F_(sL1) Motional resonance frequency hertz 7 of combination of vibrator and C_(L1) F_(sL2) Motional resonance frequency hertz 7 of combination of vibrator and C_(L2) i₁ Input current to transmission ampere 4 network L₀ Compensation inductance henry 4 shunting vibrator M_(T) Figure of merit of dimensionless 4, 10 5 transmission network ${termination} = {\frac{1}{2_{\pi}f_{s}C_{T}R_{T}} = \frac{X_{T}}{R_{T}}}$ R_(T) Shunt resistance termination ohm 4, 11a, 5 4, 6, 7, 8 of transmission network 11b, 11c, 12 R_(sL2) Standard resistor ohm 4, 5 5 7 S Detector sensitivity smallest dimensionless 12 6 detectable current change/ current x Normalized frequency dimensionless 12 ${{factor}\; x} = {{\frac{f^{2}}{f_{s}^{2}} - 1} = \frac{\Omega}{r}}$ X_(A-B) Reactance of stray ohm capacitance C_(A-B) X_(T) Reactance of C_(T) at the ohm 4 5 motional resonance ${{frequency}\; X_{T}} = \frac{1}{2_{\pi}f_{s}C_{T}}$ X_(mT) Normalized frequency factor dimensionless 5 at the frequency of maximum transmission ΔC_(L) ΔC_(L) = C_(L2) − C_(L1) farad 6, 7 Δf Δf₁ = f_(sL1) − f_(sL2) hertz 6, 7 6, 8 Δf₁ Δf₁ = f_(sL1) − f_(s) hertz 6, 7 Δf₂ Δf₁ = f_(sL2) ⁻ _(fs) hertz 6, 7 *Refers to real roots; complex roots to be disregarded.

TABLE 4 is a list of solutions for various characteristic frequencies (from IEEE 177 Standard).

Solutions for the Various Characteristic Frequencies

TABLE 4 Constituent Characteristic Equation for 57 IEEE Frequencies Meaning Condition Frequency Root 14.S1¹ f_(m) Frequency of maximum =O −2δ²(Ω + r) − lower* f_(m) admittance (minimum 2Ωr(1 − Ω) − impedance) Ω² = O f_(a) Motional (series) X₁ = O Ω = O f_(a) resonance frequency f_(r) Resonance frequency X_(e) = Ω (1 − Ω) − lower f_(r) B_(p) = O δ² = O f_(a) Antiresonance frequency X_(e) = Ω (1 − Ω) − upper f_(a) B_(p) = O δ² = O f_(p) Parallel resonance | =∞ | Ω = 1 f_(p) frequency (lossless) R₁ = O f_(n) Frequency of minimum =O −2δ²(Ω + r) − upper* f_(n) admittance (maximum 2Ωr(1 − Ω) − impedance) Ω² = O *Refers to real roots; complex roots to be disregarded

TABLE 5 is a list of losses of three classes of piezoelectric materials.

TABLE 5 Minimum Values for the Ratio Q^(r)/r to be Expected for Various Types of Piezoelectric Vibrators Type of Piezoelectric Vibrator Q = Mr r Q^(r)/r min Piezoelectric Ceramics 90-500  2-40  200 Water-Soluble 200-50,000 3-500 80 Piezoelectric Crystals Quartz 10⁴-10⁷    100-50,000 2000

TABLE 6 illustrates jaw conditions, estimated parameters of a circle based on real time measurements of complex impedance/admittance, radius (re) and offsets (ae and be) of the circle represented by measured variables Re, Ge, Xe, Be, and parameters of a reference circle plots, as described in FIGS. 25-29, based on real time measurements of complex impedance/admittance, radius (rr) and offsets (ar, br) of the reference circle represented by reference variables Rref, Gref, Xref, Bref. These values are then compared with established values for given conditions. These conditions may be: 1) open with nothing in jaws, 2) tip bite 3) full bite and staple in jaws. The equivalent circuit of the ultrasonic transducer was modeled as follows and the frequency was swept from 55 kHz to 56 kHz: Ls=L1=1.1068 H Rs=R1=311.352Ω Cs=C1=7.43265 pF and C0=C0=3.64026 nF

TABLE 6 Reference Jaw Reference Circle Plot Conditions R_(ref) (Ω) G_(ref) (μS) X_(ref) (Ω) B_(ref) (mS) Jaw open and 1.66026 64.0322 −697.309 1.63007 no loading Jaw clamped on 434.577 85.1712 −758.772 1.49569 dry chamois Jaw tip clamped 445.259 96.2179 −750.082 1.50236 on moist chamois Jaw fully clamped 137.272 1.48481 on moist chamois

In use, the ultrasonic generator sweeps the frequency, records the measured variables, and determines estimates Re, Ge, Xe, Be. These estimates are then compared to reference variables Rref, Gref, Xref, Bref stored in memory (e.g., stored in a look-up table) and determines the jaw conditions. The reference jaw conditions shown in TABLE 6 are examples only. Additional or fewer reference jaw conditions may be classified and stored in memory. These variables can be used to estimate the radius and offsets of the impedance/admittance circle.

FIG. 30 is a logic flow diagram 132120 of a process depicting a control program or a logic configuration to determine jaw conditions based on estimates of the radius (r) and offsets (a, b) of an impedance/admittance circle, in accordance with at least one aspect of the present disclosure. Initially a data base or lookup table is populated with reference values based on reference jaw conditions as described in connection with FIGS. 25-29 and TABLE 6. A reference jaw condition is set and the frequency is swept from a value below resonance to a value above resonance. The reference values Rref, Gref, Xref, Bref that define the corresponding impedance/admittance circle plot are stored in a database or lookup table. During use, under control of a control program or logic configuration a control circuit of the generator or instrument causes the frequency to sweep 132122 from below resonance to above resonance. The control circuit measures and records 132124 (e.g., stores in memory) the variables Re, Ge, Xe, Be that define the corresponding impedance/admittance circle plot and compares 132126 them to the reference values Rref, Gref, Xref, Bref stored in the database or lookup table. The control circuit determines 132128, e.g., estimates, the end effector jaw conditions based on the results of the comparison.

Live Time Tissue Classification Using Electrical Parameters Sealing Without Cutting, RF/Ultrasonic Combination Technology, Tailored Algorithms

In one aspect, the present disclosure provides an algorithm for classifying tissue into groups. A tissue type may be determined 132152 using the techniques described in FIGS. 19-21 under the heading ESTIMATING THE STATE OF THE JAW (PAD BURN THROUGH, STAPLES, BROKEN BLADE, BONE IN JAW, TISSUE IN JAW and/or FIGS. 22-30 under the heading STATE OF JAW CLASSIFIER BASED ON MODEL and/or techniques for estimating the temperature of the ultrasonic blade are described in related US Provisional Patent Application No. 62/640,417, titled TEMPERATURE CONTROL IN ULTRASONIC DEVICE AND CONTROL SYSTEM THEREFOR, to Nott et al, which is incorporated herein by reference in its entirety. The ability to classify tissue in live time will allow for tailoring algorithms to a specific tissue group. The tailored algorithms can optimize seal times and hemostasis across all tissue types. In one aspect, the present disclosure provides a sealing algorithm to provide hemostasis needed for large vessels and quickly seal smaller structures that do not need extended energy activation. The ability to classify these distinct tissue types allows for optimized algorithms for each group in live time.

In this aspect, during the first 0.75 seconds of the activation, 3 RF electrical parameters are used in a plot to classify tissue into distinct groups. These electrical parameters are: Initial RF impedance (taken at 0.15 seconds), Minimum RF impedance in first 0.75 seconds, and the amount of time the RF impedance slope is ˜0 in milliseconds. A plurality of other times that these data points are taken could be implemented. All of these data are collected in a set amount of time, and then using a Support Vector Machine (SVM) or another classification algorithm, the tissue can be classified into a distinct group in live time. Each tissue group would have an algorithm specific to it that would be implemented for the remainder of the activation. Types of SVM's include linear, polynomial, and radial basis function (RBF).

FIG. 31 is a three-dimensional graphical representation 132450 of radio frequency (RF) tissue impedance classification, in accordance with at least one aspect of the present disclosure. The x-axis represents the minimum RF impedance (Zmin) of the tissue, the y-axis represents initial RF impedance (Zinit) of the tissue, and the z-axis represents the amount of time that the derivative of the RF impedance (Z) of the tissue is approximately 0. FIG. 31 shows a grouping of large vessels 132452, e.g., carotids—thick tissue, and small vessels 132454, e.g., thyros—thin tissue, when using the three RF parameters of Initial RF impedance, Minimum RF Impedance, and the amount of time the derivative (slope) of the RF impedance is approximately zero within the first 0.75 seconds of activation. A distinction of this classification method is that the tissue type can be classified in a set amount of time. The advantage to this method is a tissue specific algorithm can be chosen towards the beginning of the activation, so specialized tissue treatment can begin before the exit out of the RF bathtub. It will be appreciated that in the context of tissue impedance under the influence of RF energy a bathtub region is a curve wherein the tissue impedance drops after the initial application of RF energy and stabilizes until the tissue begins to dry out. Thereafter the tissue impedance increases. Thus, the impedance versus time curve resembles the shape of a “bathtub.”

This data were used to train and test a Support Vector Machine to group thick and thin tissue, and accurately classified 94% of the time.

In one aspect, the present disclosure provides a device comprising one combo RF/Ultrasonic algorithm that is used for all tissue types and it has been identified that seal speeds for thin tissues are longer than necessary, however larger vessels and thicker structures could benefit from an extended activation. This classification scheme will enable the combo RF/ultrasonic device to seal small structures with optimal speeds and burst pressures, and to seal larger structures to ensure maximum hemostasis is achieved.

FIG. 32 is a three-dimensional graphical representation 132460 of radio frequency (RF) tissue impedance analysis, in accordance with at least one aspect of the present disclosure. The x=axis represents the minimum RF impedance (Zmin) of the tissue, the y-axis represents the initial RF impedance (Zinit) of the tissue, and the z-axis represents the amount of time that the derivative of the RF impedance (Z) of the tissue is approximately 0. To determine if this classification model of thick tissue 132462 versus thin tissue 132464 was robust to different tissue types, data were added for various benchtop tissue types, and the tissues grouped into two distinct groups. It is possible to separate these data into a plurality of groups if it is deemed beneficial or necessary. The different thick tissue 132462 types include, for example, carotid, jejunum, mesentery, jugular, and liver tissue. The different thin tissue 132464 types include, for example, thyro and thyro vein.

While several forms have been illustrated and described, it is not the intention of the applicant to restrict or limit the scope of the appended claims to such detail. Numerous modifications, variations, changes, substitutions, combinations, and equivalents to those forms may be implemented and will occur to those skilled in the art without departing from the scope of the present disclosure. Moreover, the structure of each element associated with the described forms can be alternatively described as a means for providing the function performed by the element. Also, where materials are disclosed for certain components, other materials may be used. It is therefore to be understood that the foregoing description and the appended claims are intended to cover all such modifications, combinations, and variations as falling within the scope of the disclosed forms. The appended claims are intended to cover all such modifications, variations, changes, substitutions, modifications, and equivalents.

The foregoing detailed description has set forth various forms of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, and/or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. Those skilled in the art will recognize that some aspects of the forms disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as one or more program products in a variety of forms, and that an illustrative form of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution.

Instructions used to program logic to perform various disclosed aspects can be stored within a memory in the system, such as dynamic random access memory (DRAM), cache, flash memory, or other storage. Furthermore, the instructions can be distributed via a network or by way of other computer readable media. Thus a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), but is not limited to, floppy diskettes, optical disks, compact disc, read-only memory (CD-ROMs), and magneto-optical disks, read-only memory (ROMs), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or a tangible, machine-readable storage used in the transmission of information over the Internet via electrical, optical, acoustical or other forms of propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.). Accordingly, the non-transitory computer-readable medium includes any type of tangible machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).

As used in any aspect herein, the term “control circuit” may refer to, for example, hardwired circuitry, programmable circuitry (e.g., a computer processor comprising one or more individual instruction processing cores, processing unit, processor, microcontroller, microcontroller unit, controller, digital signal processor (DSP), programmable logic device (PLD), programmable logic array (PLA), or field programmable gate array (FPGA)), state machine circuitry, firmware that stores instructions executed by programmable circuitry, and any combination thereof. The control circuit may, collectively or individually, be embodied as circuitry that forms part of a larger system, for example, an integrated circuit (IC), an application-specific integrated circuit (ASIC), a system on-chip (SoC), desktop computers, laptop computers, tablet computers, servers, smart phones, etc. Accordingly, as used herein “control circuit” includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitry forming a memory device (e.g., forms of random access memory), and/or electrical circuitry forming a communications device (e.g., a modem, communications switch, or optical-electrical equipment). Those having skill in the art will recognize that the subject matter described herein may be implemented in an analog or digital fashion or some combination thereof.

As used in any aspect herein, the term “logic” may refer to an app, software, firmware and/or circuitry configured to perform any of the aforementioned operations. Software may be embodied as a software package, code, instructions, instruction sets and/or data recorded on non-transitory computer readable storage medium. Firmware may be embodied as code, instructions or instruction sets and/or data that are hard-coded (e.g., nonvolatile) in memory devices.

As used in any aspect herein, the terms “component,” “system,” “module” and the like can refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution.

As used in any aspect herein, an “algorithm” refers to a self-consistent sequence of steps leading to a desired result, where a “step” refers to a manipulation of physical quantities and/or logic states which may, though need not necessarily, take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It is common usage to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. These and similar terms may be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities and/or states.

A network may include a packet switched network. The communication devices may be capable of communicating with each other using a selected packet switched network communications protocol. One example communications protocol may include an Ethernet communications protocol which may be capable permitting communication using a Transmission Control Protocol/Internet Protocol (TCP/IP). The Ethernet protocol may comply or be compatible with the Ethernet standard published by the Institute of Electrical and Electronics Engineers (IEEE) titled “IEEE 802.3 Standard”, published in December, 2008 and/or later versions of this standard. Alternatively or additionally, the communication devices may be capable of communicating with each other using an X.25 communications protocol. The X.25 communications protocol may comply or be compatible with a standard promulgated by the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T). Alternatively or additionally, the communication devices may be capable of communicating with each other using a frame relay communications protocol. The frame relay communications protocol may comply or be compatible with a standard promulgated by Consultative Committee for International Telegraph and Telephone (CCITT) and/or the American National Standards Institute (ANSI). Alternatively or additionally, the transceivers may be capable of communicating with each other using an Asynchronous Transfer Mode (ATM) communications protocol. The ATM communications protocol may comply or be compatible with an ATM standard published by the ATM Forum titled “ATM-MPLS Network Interworking 2.0” published August 2001, and/or later versions of this standard. Of course, different and/or after-developed connection-oriented network communication protocols are equally contemplated herein.

Unless specifically stated otherwise as apparent from the foregoing disclosure, it is appreciated that, throughout the foregoing disclosure, discussions using terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

One or more components may be referred to herein as “configured to,” “configurable to,” “operable/operative to,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Those skilled in the art will recognize that “configured to” can generally encompass active-state components and/or inactive-state components and/or standby-state components, unless context requires otherwise.

The terms “proximal” and “distal” are used herein with reference to a clinician manipulating the handle portion of the surgical instrument. The term “proximal” refers to the portion closest to the clinician and the term “distal” refers to the portion located away from the clinician. It will be further appreciated that, for convenience and clarity, spatial terms such as “vertical”, “horizontal”, “up”, and “down” may be used herein with respect to the drawings. However, surgical instruments are used in many orientations and positions, and these terms are not intended to be limiting and/or absolute.

Those skilled in the art will recognize that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to claims containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that typically a disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms unless context dictates otherwise. For example, the phrase “A or B” will be typically understood to include the possibilities of “A” or “B” or “A and B.”

With respect to the appended claims, those skilled in the art will appreciate that recited operations therein may generally be performed in any order. Also, although various operational flow diagrams are presented in a sequence(s), it should be understood that the various operations may be performed in other orders than those which are illustrated, or may be performed concurrently. Examples of such alternate orderings may include overlapping, interleaved, interrupted, reordered, incremental, preparatory, supplemental, simultaneous, reverse, or other variant orderings, unless context dictates otherwise. Furthermore, terms like “responsive to,” “related to,” or other past-tense adjectives are generally not intended to exclude such variants, unless context dictates otherwise.

It is worthy to note that any reference to “one aspect,” “an aspect,” “an exemplification,” “one exemplification,” and the like means that a particular feature, structure, or characteristic described in connection with the aspect is included in at least one aspect. Thus, appearances of the phrases “in one aspect,” “in an aspect,” “in an exemplification,” and “in one exemplification” in various places throughout the specification are not necessarily all referring to the same aspect. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner in one or more aspects.

Any patent application, patent, non-patent publication, or other disclosure material referred to in this specification and/or listed in any Application Data Sheet is incorporated by reference herein, to the extent that the incorporated materials is not inconsistent herewith. As such, and to the extent necessary, the disclosure as explicitly set forth herein supersedes any conflicting material incorporated herein by reference. Any material, or portion thereof, that is said to be incorporated by reference herein, but which conflicts with existing definitions, statements, or other disclosure material set forth herein will only be incorporated to the extent that no conflict arises between that incorporated material and the existing disclosure material.

In summary, numerous benefits have been described which result from employing the concepts described herein. The foregoing description of the one or more forms has been presented for purposes of illustration and description. It is not intended to be exhaustive or limiting to the precise form disclosed. Modifications or variations are possible in light of the above teachings. The one or more forms were chosen and described in order to illustrate principles and practical application to thereby enable one of ordinary skill in the art to utilize the various forms and with various modifications as are suited to the particular use contemplated. It is intended that the claims submitted herewith define the overall scope.

Various aspects of the subject matter described herein are set out in the following numbered examples:

Example 1. A method of classifying a tissue in live time, the method comprising:

activating, by a processor or control circuit, a radio frequency (RF) instrument for a first period of time T1, wherein the RF instrument contacts the tissue;

plotting, by the processor or control circuit, at least three electrical parameters associated with the tissue in contact with the RF instrument to classify the tissue into distinct groups; and

applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time.

Example 2. The method of Example 1, wherein plotting, by the processor or control circuit, at least three electrical parameters associated with the tissue in contact with the RF instrument comprises plotting, by the processor or control circuit, an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that the RF impedance slope is ˜0.

Example 3. The method of any one or more of Examples 1 through 2, further comprising collecting, by the processor or control circuit, data associated with the at least three electrical parameters in a predetermined amount of time.

Example 4. The method of Example 3, wherein collecting, by the processor or control circuit, data associated with the at least three electrical parameters in a predetermined amount of time comprises collecting, by the processor or control circuit, data associated with the at least three electrical parameters in within a first 0.75 seconds after activation of the radio frequency (RF) instrument.

Example 5. The method of any one or more of Examples 1 through 4, wherein applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time comprises applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time using a support vector machine algorithm.

Example 6. The method of Example 5, wherein applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time using a support vector machine algorithm comprises applying, by the processor or control circuit, a classification algorithm to classify the tissue into a distinct group in live time using a linear basis function, a polynomial basis function, or a radial basis function.

Example 7. The method of any one or more of Examples 1 through 6, further comprising applying, by the processor or control circuit, an activation algorithm specific to each tissue group after the first period T1.

Example 8. A surgical instrument comprising:

a radio frequency (RF) instrument comprising an end effector; and

a generator configured to supply power to the end effector, wherein the generator comprises a control circuit configured to:

-   -   activate the radio frequency (RF) instrument for a first period         of time T1, wherein the RF instrument contacts the tissue;     -   plot at least three electrical parameters associated with the         tissue in contact with the RF instrument to classify the tissue         into distinct groups; and     -   apply a classification algorithm to classify the tissue into a         distinct group in live time.

Example 9. The surgical instrument of Example 8, wherein the at least three electrical parameters associated with the tissue in contact with the RF instrument comprise an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that the RF impedance slope is ˜0.

Example 10. The surgical instrument of any one or more of Examples 8 through 9, wherein the control circuit is further configured to collect data associated with the at least three electrical parameters in a predetermined amount of time.

Example 11. The surgical instrument of Example 10, wherein the predetermined amount of time comprises a first 0.75 seconds after activation of the radio frequency (RF) instrument.

Example 12. The surgical instrument of any one or more of Examples 8 through 11, wherein the control circuit is further configured to classify the tissue into a distinct group in live time using a support vector machine algorithm.

Example 13. The surgical instrument of Example 12, wherein the support vector machine algorithm comprises a linear basis function, a polynomial basis function, or a radial basis function.

Example 14. The surgical instrument of any one or more of Examples 8 through 13, wherein the control circuit is further configured to apply an activation algorithm specific to each tissue group after the first period T1.

Example 15. A generator for a surgical instrument, wherein the surgical instrument comprises a radio frequency (RF) instrument comprising an end effector, the generator comprising:

a control circuit configured to:

-   -   activate the radio frequency (RF) instrument for a first period         of time T1, wherein the RF instrument contacts a tissue;     -   plot at least three electrical parameters associated with the         tissue in contact with the RF instrument to classify the tissue         into distinct groups; and     -   apply a classification algorithm to classify the tissue into a         distinct group in live time.

Example 16. The generator for a surgical instrument of Example 15, wherein the at least three electrical parameters associated with the tissue in contact with the RF instrument comprise an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that the RF impedance slope is ˜0.

Example 17. The generator for a surgical instrument of any one or more of Examples 15 through 16, wherein the control circuit is further configured to collect data associated with the at least three electrical parameters in a predetermined amount of time.

Example 18. The generator for a surgical instrument of Example 17, wherein the predetermined amount of time comprises a first 0.75 seconds after activation of the radio frequency (RF) instrument.

Example 19. The generator for a surgical instrument of any one or more of Examples 15 through 18, wherein the control circuit is further configured to classify the tissue into a distinct group in live time using a support vector machine algorithm.

Example 20. The generator for a surgical instrument of Example 19, wherein the support vector machine algorithm comprises a linear basis function, a polynomial basis function, or a radial basis function.

Example 21. The generator for a surgical instrument of any one or more of Examples 15 through 20, wherein the control circuit is further configured to apply an activation algorithm specific to each tissue group after the first period T1. 

The invention claimed is:
 1. A method of classifying a tissue in live time, the method comprising: activating, by a processor, a radio frequency (RF) instrument for a first period of time T1, wherein the RF instrument contacts the tissue; plotting together, by the processor, at least three electrical parameters associated with the tissue in contact with the RF instrument to classify the tissue into distinct groups; and applying, by the processor, a classification algorithm to classify the tissue into a distinct group in live time.
 2. The method of claim 1, wherein plotting together, by the processor, the at least three electrical parameters associated with the tissue in contact with the RF instrument comprises plotting together, by the processor, an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that an RF impedance slope of the tissue is ˜0.
 3. The method of claim 1, further comprising collecting, by the processor, data associated with the at least three electrical parameters in a predetermined amount of time.
 4. The method of claim 3, wherein collecting, by the processor, data associated with the at least three electrical parameters in a predetermined amount of time comprises collecting, by the processor, the data associated with the at least three electrical parameters within a first 0.75 seconds after activation of the RF instrument.
 5. The method of claim 1, wherein applying, by the processor, a classification algorithm to classify the tissue into a distinct group in live time comprises using, by the processor, a support vector machine algorithm.
 6. The method of claim 5, wherein using, by the processor, a support vector machine algorithm comprises using, by the processor, a linear basis function, a polynomial basis function, or a radial basis function.
 7. The method of claim 1, further comprising applying, by the processor, an activation algorithm specific to each tissue group after the first period of time T1.
 8. A surgical instrument comprising: a radio frequency (RF) instrument comprising an end effector; and a generator configured to supply power to the end effector, wherein the generator comprises a control circuit configured to: activate the RF instrument for a first period of time T1, wherein the RF instrument is configured to contact a tissue; plot together at least three electrical parameters associated with the tissue in contact with the RF instrument to classify the tissue into distinct groups; and apply a classification algorithm to classify the tissue into a distinct group in live time.
 9. The surgical instrument of claim 8, wherein the at least three electrical parameters associated with the tissue in contact with the RF instrument comprise an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that an RF impedance slope of the tissue is ˜0.
 10. The surgical instrument of claim 8, wherein the control circuit is further configured to collect data associated with the at least three electrical parameters in a predetermined amount of time.
 11. The surgical instrument of claim 10, wherein the predetermined amount of time comprises a first 0.75 seconds after activation of the RF instrument.
 12. The surgical instrument of claim 8, wherein the control circuit is further configured to classify the tissue into the distinct group in live time using a support vector machine algorithm.
 13. The surgical instrument of claim 12, wherein the support vector machine algorithm comprises a linear basis function, a polynomial basis function, or a radial basis function.
 14. The surgical instrument of claim 8, wherein the control circuit is further configured to apply an activation algorithm specific to each tissue group after the first period of time T1.
 15. A generator for a surgical instrument, wherein the surgical instrument comprises a radio frequency (RF) instrument comprising an end effector, the generator comprising: a control circuit configured to: activate the RF instrument for a first period of time T1, wherein the RF instrument is configured to contact a tissue; plot together at least three electrical parameters associated with the tissue in contact with the RF instrument to classify the tissue into distinct groups; and apply a classification algorithm to classify the tissue into a distinct group in live time.
 16. The generator for the surgical instrument of claim 15, wherein the at least three electrical parameters associated with the tissue in contact with the RF instrument comprise an initial RF impedance of the tissue, a minimum RF impedance of the tissue, and an amount of time in milliseconds that an RF impedance slope of the tissue is ˜0.
 17. The generator for the surgical instrument of claim 15, wherein the control circuit is further configured to collect data associated with the at least three electrical parameters in a predetermined amount of time.
 18. The generator for the surgical instrument of claim 17, wherein the predetermined amount of time comprises a first 0.75 seconds after activation of the RF instrument.
 19. The generator for the surgical instrument of claim 15, wherein the control circuit is further configured to classify the tissue into the distinct group in live time using a support vector machine algorithm.
 20. The generator for the surgical instrument of claim 19, wherein the support vector machine algorithm comprises a linear basis function, a polynomial basis function, or a radial basis function.
 21. The generator for the surgical instrument of claim 15, wherein the control circuit is further configured to apply an activation algorithm specific to each tissue group after the first period of time T1. 